Color Image

We present a new adaptation of Retinex to enhance the rendering of high dynamic range digital color images. The image is processed using an adaptive Gaussian filter. The shape of the filter basis is adapted to follow the high contrasted edges of the image. In this way, the artifacts introduced by a circularly symmetric filter at the border of high contrasted areas are reduced. This method provides a way of rendering natural images that is inspired by human local adaptation. It is included into a framework that takes raw linear images or radiance maps and outputs 24-bit images rendered for display.

We propose a new method to render high dynamic range images that models global and local adaptation of the human visual system. Our method is based on the centersurround Retinex model. The novelties of our method is first to use an adaptive filter, whose shape follows the image high contrast edges, thus reducing halo artifacts common to other methods. Secondly, only the luminance channel is processed, which is defined by the first component of a principal component analysis. Principal component analysis provides orthogonality between channels and thus reduces the chromatic changes caused by the modification of luminance. We show that our method efficiently renders high dynamic range images and we compare our results with the current state of the art.

Fern species under family Thelypteridaceae and Nephrolepidaceae tend to have similarities when it comes to leaf morphology. Due to their cohesive morphological appearance, species under these two families are hard to distinguish from the other. While qualitative descriptions in leaf shape could aid in understanding the identification, evolution and development of ferns, it is argued that the quantitative description of the shape of the pinna is a good measure in the classification of ferns by delineating species. This study was therefore conducted to quantitatively describe the pinna shape of five species of ferns under family Thelypteridaceae and four species under family Nephrolepidaeceae by Elliptic Fourier Analysis (EFA). Significant shape variations were observed within and between species specifically in width, apex and base of the pinna. Species found on shaded habitat were observed to have broader pinna lamina compared to elongated and narrow pinna of fern species found on open areas. Light availability is argued to possibly influenced the pinna shapes of the ferns. Results of this study have shown the importance of EFA in the quantitative description of biological shape that aid in understanding the relationships of the fern species.

Color photography was originally rare and expensive, and color images became more popular only in the middle of the 20th century. It has indeed become even more popular since yet black-and-white remains a niche market for people who use the medium for artistic purposes. Even in today's modern era, there is importance of grayscale and black-and-white images. Since the advent of color, black-and-white connotes something nostalgic, historic and anachronistic. There are three basic and primitive techniques to convert a color image into its gray level equivalent -the lightness method, the average method and the luminosity method. Here, we propose some novel approaches of converting a color image into grayscale image, based on varying pixel depth.

This paper presents a perceptual model to predict the visual distinction between a pair of color images which correlates well with the human observer's evaluation. Its basic assumptions are to be in agreement with spatial-frequency channels models quite successful for the detection of visual patterns. The model was applied to predict the perceived quality of a basic pattern as well as the visibility of the artifacts in JPEG reconstructions. The rank order produced by the PID error and other quantitative measure are compared with the reference rank order as given by a subjective rating performed by photointerpreters.

This paper presents a perceptual model to predict the visual distinction between a pair of color images which correlates well with the human observer's evaluation. Its basic assumptions are to be in agreement with spatial-frequency channels models quite successful for the detection of visual patterns. The model was applied to predict the perceived quality of a basic pattern as well as the visibility of the artifacts in JPEG reconstructions. The rank order produced by the PID error and other quantitative measure are compared with the reference rank order as given by a subjective rating performed by photointerpreters.

The field sequential color (FSC) mechanism can effectively generate multi-primary color fields in temporal sequence to form a full-color image. Color breakup (CBU), however, has appeared intrinsically in conventional FSC displays to degrade visual qualities. A novel CBU suppression method, color fields arrangement (CFA), was proposed to eliminate the artifacts for FSC liquid crystal displays (LCDs). The modified order of consecutive color fields results in superimposed color images on a retina without CBU. Additionally, the 4-CFA method with a field rate of 240 Hz was found to avoid the flicker phenomenon on static images. The proposed method was successfully implemented on a 5.6-in optically compensated bend (OCB) LC panel. Our results confirm that the visibility of CBU artifacts can be reduced as the evaluation of dynamic and static models.

This research work introduces a method of using color thresholds to identify two-dimensional images in MATLAB using the RGB Color model to recognize the Color preferred by the user in the picture. Methodologies including image color detection convert a 3-D RGB Image into a Gray-scale Image, at that point subtract the two pictures to obtain a 2-D black-and-white picture, filtering the noise picture elements using a median filter, detecting with a connected component mark digital pictures in the connected area and utilize the bounding box and its properties to calculate the metric for every marking area. In addition, the shade of the picture element is identified by examining the RGB value of every picture element present in the picture. Color Detection algorithm is executed utilizing the MATLAB  Picture handling Toolkit. The result of this implementation can be used in as a bit of security applications such as spy robots, object tracking, Color-based object isolation, and intrusion d...

The underwater images are very crucial in locating and monitoring the various activities in the sea and marine life. The underwater environment is complex relative to the terrestrial environment as there is no source of natural light there, thus it needs an artificial source of light. The quality of the images captured by the underwater equipment is not ideal due to the water properties and the presence of impurities in the water. Typically, the underwater images encounter issues like low contrast, color distortions and low visual quality. In order to tackle these issues, underwater image enhancement has emerged as a powerful technique for overcoming these issues and enhancing underwater images. The combination of artificial intelligence and underwater imaging has opened new avenues for research in recent years. This paper reviews the various techniques of underwater image enhancement and presents an experimental analysis of various enhancement methods in terms of qualitative and quantitative evaluation metrics on the UIEB dataset. The hybrid techniques such as u-shape transformer and unet with attention mechanisms strengthen the feature extraction and improve the quality of enhancement.

The concept of edge detection is an aspect of image processing that attracts lot of attention as it is known to significantly reduce the amount of data and filters out useless information in an image. Boundaries of an image are usually characterized by edges, which in turn preserve the important structural properties in an image. Color is a property that is imminent and which simplifies description, object identification and extraction from scene thereby making colour image edge detection to have widespread in colour image segmentation. This paper reviews the method of RGB image edge detection with an improvement on colour image edge detection by transformation from RGB colour space to YUV colour space, processing the Ycomponent of the YUV image accompanied with histogram equalization transform of the processed image. The experiment results show that the improved method provides a faster method for colour image detection whilst retaining more features when compared to RGB image detection.

Background Multiphoton microscopy (MPM) offers many advantages over conventional wide-field and confocal laser scanning microscopy (CLSM) for imaging biological samples such as 3D resolution of excitation, reduced phototoxicity, and deeper tissue imaging. However, adapting MPM for critical multi-color measurements presents a challenge because of the largely overlapping two-photon absorption (TPA) peaks of common biological fluorophores. Currently, most multi-color MPM relies on the absorbance at one intermediate wavelength of multiple dyes, which introduces problems such as decreased and unequal excitation efficiency across the set of dyes. Results Here we describe an MPM system incorporating two, independently controlled sources of two-photon excitation whose wavelengths are adjusted to maximally excite one dye while minimally exciting the other. We report increased signal-to-noise ratios and decreased false positive emission bleed-through using this novel multiple-excitation MPM (...

Background Two-photon dual-color imaging of tissues and cells labeled with fluorescent proteins (FPs) is challenging because most two-photon microscopes only provide one laser excitation wavelength at a time. At present, methods for two-photon dual-color imaging are limited due to the requirement of large differences in Stokes shifts between the FPs used and their low two-photon absorption (2PA) efficiency. Results Here we present a new method of dual-color two-photon microscopy that uses the simultaneous excitation of the lowest-energy electronic transition of a blue fluorescent protein and a higher-energy electronic transition of a red fluorescent protein. Conclusion Our method does not require large differences in Stokes shifts and can be extended to a variety of FP pairs with larger 2PA efficiency and more optimal imaging properties.

Digital image watermarking is a crucial technique used to protect the integrity and ownership of digital images by embedding imperceptible watermarks into the image content. This review concentrates on the utilization of discrete cosine transform (DCT) and discrete wavelet transform (DWT) in digital image watermarking schemes. DCT, widely used in image compression like JPEG, is an attractive choice for watermarking, modifying DCT coefficients with minimal impact on image quality. On the other hand, DWT offers multiresolution representation, enabling better localization and robustness against attacks. DWT-based methods use wavelet coefficients to embed watermarks in specific frequency bands or image regions. The review examines the strengths and weaknesses of DCT and DWT in digital image watermarking, exploring algorithms and approaches proposed in the literature. It also addresses challenges like attacks, synchronization, and robustness to image processing. Additionally, a comparative analysis of DCT and DWT-based methods considers imperceptibility, robustness, capacity, and computational complexity. By offering valuable insights, this review aids researchers and practitioners in implementing secure and efficient digital image watermarking solutions.

Since there are a number of color spaces, it has always been a big question to choose one for watermarking. The aim of this work is to find out better color space, among the frequently used one, under the same condition. Comparative performance analysis of color image watermarking technique in color channels of RGB, YUV, YCbCrcolor spaces was studied. For this purpose, color channels were watermarked using single level discrete wavelet transform-singular value decomposition (DWT-SVD). PSNR, and SSIM were used to test the imperceptibility of watermarked images. PSNR and NCC were used to measure the similarity of extracted and original watermarks.The maximum recorded PSNR value is 62.372 for R channel of RGB color space with SSIM value equal to 0.9709. Color channels of YCbCr color space were observed to be more robust and transparent as watermark image is best recovered from YCbCr color space with NCC values in the range 0.86 to 0.877 and SSIM values in the range 0.546to 0.554 under ...

Listless SPECK (LSPECK) is a low complexity image coding algorithm compared to SPECK. The problem of LSPECK is that, it encode each insignificant subband by a zero. Therefore, these block based coders codes as many zeros as the number of insignificant subbands. This gives rise to many zeros at the encoder output on early bit plane passes. By looking at the statistics of transformed images, the number of significant coefficients at some of the higher bitplanes are likely to be very few. We propose a variant of LSPECK algorithm, called as Improved LSPECK (ILSPECK), that code a single zero to several insignificant subbands. This reduces the length of the output bit string as well as encoding/decoding time. Further, ILSPECK algorithm is coupled with discrete tchebichef transform (DTT). The propose new coder called as Hierarchical Listless DTT (HLDTT), preserves most of the properties of wavelet coders. Extensive simulations on various kind of images shows the effictiveness of our coder.

This paper presents a Discrete Tchebichef Transform (DTT) based hybrid embedded coder for image compression applications. In this coder, DTT is coupled with Set partitioning in hierarchical coding techniques (SPIHT). Further, human visual system (HVS) with appropriate perceptual weights are applied to improve the perceptual quality of the reconstructed image. The compression and image reconstructon performance is compared with some the state-of-the-art coders in the literature. Extensive simulations on various kinds of images shows convincingly that, the proposed coder outperforms most of the coders.

This paper presents a listless implementation of wavelet based block tree coding (WBTC) algorithm of varying root block sizes. WBTC algorithm improves the image compression performance of set partitioning in hierarchical trees (SPIHT) at lower rates by efficiently encoding both inter and intra scale correlation using block trees. Though WBTC lowers the memory requirement by using block trees compared to SPIHT, it makes use of three ordered auxiliary lists. This feature makes WBTC undesirable for hardware implementation; as it needs a lot of memory management when the list nodes grow exponentially on each pass. The proposed listless implementation of WBTC algorithm uses special markers instead of lists. This reduces dynamic memory requirement by 88% with respect to WBTC and 89% with respect to SPIHT. The proposed algorithm is combined with discrete cosine transform (DCT) and discrete wavelet transform (DWT) to show its superiority over DCT and DWT based embedded coders, including JPEG 2000 at lower rates. The compression performance on most of the standard test images is nearly same as WBTC, and outperforms SPIHT by a wide margin particularly at lower bit rates.

Most commercial digital cameras use a single electronic sensor overlaid with a color filter array (CFA) to capture imagery. Since only one primary color is sampled in each pixel, the missing color primaries must be reconstructed by interpolation. In this paper, an adaptive demosaicking scheme for CFA interpolation is proposed. The scheme uses intra-channel correlation, color difference correlation, constant hue, and luminance-color difference correlation is proposed. A rough interpolation is first implemented by bilinear interpolation. Then the color difference correlation and constant hue are successively used to update the missing color primaries. To obtain high quality color images, an adaptive algorithm using luminance-color difference correlation and the information of edge direction is iteratively applied to improve the image quality around the edges. Simulation results demonstrate that the image quality of the proposed algorithm is better than that of the approach using color difference correlation in terms of peak signal-to-noise ratio (PSNR).

Human eyes are not perfect sensors for perceiving color images shown in 2-D display monitors or represented as photo prints. The maximum extent of variation that is barely noticeable with average human eyes is the error-visibility threshold of the pixel or the so-called just noticeable distortion (JND) of the pixel. In this paper, a color visual model based on characteristics of human visual system (HVS) is proposed to estimate the JND profiles of color images. The contrast masking effect and texture masking effect of color images in uniform color space is measured to optimize the estimation of error-visibility threshold in chromatic channel. A subjective test that compares the perceptual quality of JND-contaminated image with that of the original color image verifies that the validity of the proposed color visual model.

Most commercial digital cameras use a single electronic sensor overlaid with a color filter array (CFA) to capture imagery. Since only one primary color is sampled in each pixel, the missing color primaries must be reconstructed by interpolation. In this paper, an adaptive demosaicking scheme for CFA interpolation is proposed. The scheme uses intra-channel correlation, color difference correlation, constant hue, and luminance-color difference correlation is proposed. A rough interpolation is first implemented by bilinear interpolation. Then the color difference correlation and constant hue are successively used to update the missing color primaries. To obtain high quality color images, an adaptive algorithm using luminance-color difference correlation and the information of edge direction is iteratively applied to improve the image quality around the edges. Simulation results demonstrate that the image quality of the proposed algorithm is better than that of the approach using color difference correlation in terms of peak signal-to-noise ratio (PSNR).

AbstractÐWe present a brute-force ray tracing system for interactive volume visualization. The system runs on a conventional (distributed) shared-memory multiprocessor machine. For each pixel we trace a ray through a volume to compute the color for that pixel. Although this method has high intrinsic computational cost, its simplicity and scalability make it ideal for large datasets on current high-end parallel systems. To gain efficiency several optimizations are used including a volume bricking scheme and a shallow data hierarchy. These optimizations are used in three separate visualization algorithms: isosurfacing of rectilinear data, isosurfacing of unstructured data, and maximum-intensity projection on rectilinear data. The system runs interactively (i.e., several frames per second) on an SGI Reality Monster. The graphics capabilities of the Reality Monster are used only for display of the final color image.

We show that it is feasible to perform interactive isosurfacing of very large rectilinear datasets with brute-force ray tracing on a conventional (distributed) shared-memory multiprocessor machine. Rather than generate geometry representing the isosurface and render with a z-buffer, for each pixel we trace a ray through a volume and do an analytic isosurface intersection computation. Although this method has a high intrinsic computational cost, its simplicity and scalability make it ideal for large datasets on current high-end systems. Incorporating simple optimizations, such as volume bricking and a shallow hierarchy, enables interactive rendering (i.e. 10 frames per second) of the 1GByte full resolution Visible Woman dataset on an SGI Reality Monster. The graphics capabilities of the reality monster are used only for display of the final color image.

In this paper, an effective color detection technique is presented. Color detection techniques are widely used in the image processing area, especially for measuring the amount of a certain color after some modifications on an image to check wither it gets enhanced or not. We test this technique on images that were carried out in a physical laboratory during some experiments. Moreover, we will use this technique to calculate the percentage of the red color before and after “red-eye” removal process. Finally, a comparison of these techniques with some other methods has been achieved.

To address the non-negativity dropout problem of quaternion models, a novel quasi non-negative quaternion matrix factorization (QNQMF) model is presented for color image processing. To implement QNQMF, the quaternion projected gradient algorithm and the quaternion alternating direction method of multipliers are proposed via formulating QNQMF as the non-convex constraint quaternion optimization problems. Some properties of the proposed algorithms are studied. The numerical experiments on the color image reconstruction show that these algorithms encoded on the quaternion perform better than these algorithms encoded on the red, green and blue channels. Furthermore, we apply the proposed algorithms to the color face recognition. Numerical results indicate that the accuracy rate of face recognition on the quaternion model is better than on the red, green and blue channels of color image as well as single channel of gray level images for the same data, when large facial expressions and shooting angle variations are presented.

Densitometry, spectrophotometry, profile making and o means comprise the powerful instrumentation for provid the proper quality of color reproduction. Various color str and bars are effectively used for a printing stage normaliza according to the recommendations of a standard. Effect o screening process on a resulting quality is, however, m concealed within a total halftone imaging estimations. At same time, the screening stage share in such paramete final print as tone rendition, dot gain, resolution, fine de distortion, structure uniformity, moire characteristics, etc. be evaluated with a minimum or even no relation to the o processes involved in an image reproduction. The prob are discussed of a proper selection the screening techniq a particular job or printing technology, of determining basic parameters and their estimation criteria, of defining development directions in digital screening technology.

Digital images, especially color images, are very widely used, as well as traded via Internet, e-mail and posting on websites. Images have a large size which allows embedding secret messages of large size, so they are a good medium for digital steganography. The main goal of steganalysis is to detect the presence of hidden messages in digital media. In this paper, we propose a steganalysis method based on color feature correlation and machine learning classification. Fusing features with features obtained from color-rich models allows to increase the detectability of hidden messages in the color images. Our novel proposition uses the correlation between different channels of color images. Features are extracted from the channel correlation and co-occurrence correlation. In this work, all stego images are created with a range of different payload sizes using two steganography S-UNIWARD and WOW algorithms. To validate the proposed method, his efficiency is demonstrated by comparison with color rich model steganalysis.

This article deals with color images steganalysis based on machine learning. The proposed approach enriches the features from the Color Rich Model by adding new features obtained by applying steerable Gaussian filters and then computing the co-occurrence of pixel pairs. Adding these new features to those obtained from Color-Rich Models allows us to increase the detectability of hidden messages in color images. The Gaussian filters are angled in different directions to precisely compute the tangent of the gradient vector. Then, the gradient magnitude and the derivative of this tangent direction are estimated. This refined method of estimation enables us to unearth the minor changes that have occurred in the image when a message is embedded. The efficiency of the proposed framework is demonstrated on three stenographic algorithms designed to hide messages in images: S-UNIWARD, WOW, and Synch-HILL. Each algorithm is tested using different payload sizes. The proposed approach is compared to three color image steganalysis methods based on computation features and Ensemble Classifier classification: the Spatial Color Rich Model, the CFA-aware Rich Model and the RGB Geometric Color Rich Model.

It is common practice for digital image capture systems to use a small number of de-facto-standard test targets. Unfortunately, however, color (spectral-) characteristics of the colorants used may differ from those for the population of object/scenes to be captured. This can lead to poor color calibration of the system. A second limitation of current color-capture evaluation arises when the same set of color stimuli (color patches) are used to calibrate the color capture and to evaluate the residual color errors. When the same color-target is used, the reported color-encoding errors will usually be lower than those observed in normal image capture. This is because we are, in effect, 'teaching to the test', as when a student is prepared for test taking, rather than subject mastery. We can approach this under-reporting of color error as a measurement bias. We can treat color-correction (e.g. by a colorprofile) as being a statistical model relating the detected image values and their intended ('correct') pixel values. Using a statistical approach we adopt a validation method aimed at determining the extent to which this model relationship between variables (the regression model) provides an acceptable description of the data. For our color-imaging case, the equivalent step would be to test the computed color-correction (ICC profile) with color patches that are independent of those used to build the profile. We demonstrate a candidate strategy for selecting these test colors, and an example of a validation set of colors chosen to be distinct from the calibration set in the popular ColorChecker SG.

The design of a system for multispectral image capture will be influenced by the imaging application, such as image archiving, vision research, illuminant modification or improved (trichromatic) color reproduction. A key aspect of the system performance is the effect of noise, or error, when acquiring multiple color image records and processing of the data. This research provides an analysis that allows the prediction of the image-noise characteristics of systems for the capture of multispectral images. The effects of both detector noise and image processing quantization on the color information are considered, as is the correlation between the errors in the component signals. The above multivariate error-propagation analysis is then applied to an actual prototype system. Sources of image noise in both digital camera and image processing are related to colorimetric errors. Recommendations for detector characteristics and image processing for future systems are then discussed.

The concept of edge detection is an aspect of image processing that attracts lot of attention as it is known to significantly reduce the amount of data and filters out useless information in an image. Boundaries of an image are usually characterized by edges, which in turn preserve the important structural properties in an image. Color is a property that is imminent and which simplifies description, object identification and extraction from scene thereby making colour image edge detection to have widespread in colour image segmentation. This paper reviews the method of RGB image edge detection with an improvement on colour image edge detection by transformation from RGB colour space to YUV colour space, processing the Ycomponent of the YUV image accompanied with histogram equalization transform of the processed image. The experiment results show that the improved method provides a faster method for colour image detection whilst retaining more features when compared to RGB image detection.

This paper presents a novel watermarking scheme for authentication of digital color images in social networks. The procedure consists of the embedding of a binary watermark image, containing the owner information, into the image to be authenticated. In order to minimize the artifacts in the host image the process is carried out in the wavelets domain. Concretely, the watermark embedding is performed in the HL4 and LH4 sub-band coefficients of the red, green and blue channels of the original image, based on an optimal channel selection quantization technique. To ensure a high robustness to tampering and malicious attacks a key-based pixel shuffling mechanism is further used. The reverse process is likewise identified for the extraction of the watermark from the authenticated image. Both embedding and extraction procedures are benchmarked on diverse color images and under the effects of different types of attacks, including geometric, non-geometric, and JPEG compression transformations. The proposed scheme proves to support imperceptible watermarking, while also showing a high resiliency to common image processing operations.

The objective of this paper is to present a system to communicate hidden information among different users by means of images. The tasks that the system is able to carry on can be divided in two different groups of utilities, implemented in java. The first group of utilities are related with the possibility to hide information in color images, using a steganographic function based on the least significant bit (LSB) methods. The second group of utilities allows us to communicate with other users with the aim to send or receive images, where some information have been previously embedded. Thus, this is the most significant characteristic of the implementa- tion, we have built an environment where we join the email capabilities to send and receive text and images as attached files, with the main objective of hiding information.

Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system.

The identification and classification of seeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in ocular inspection one should consider seed size, shape, color and texture, which can be obtained from seed images. In this work we complement previous studies on the discriminating power of these characteristics for the unique identification of seeds of 57 weed species. In particular, we discuss the possibility of improving the naïve Bayes and artificial neural network classifiers previously developed in order to avoid the use of color features as classification parameters. Morphological and textural seed characteristics can be obtained from black and white images, which are easier to process and require a cheaper hardware than color ones. To this end, we boost the classification methods by means of the AdaBoost.M1 technique, and compare the results obtained with the performance achieved when using color images. We conclude that boosting the naïve Bayes and neural classifiers does not fully compensate the discriminating power of color features. However, the improvement in classification accuracy might be enough to make the classifier still acceptable in practical applications.

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, each with its own strengths and weaknesses. The bioinformatics offers innovative solutions for data security, especially in the realm of cryptography. By employing DNA sequences as data carriers, bioinformatics techniques can significantly enhance the protection of confidential data. The goal of This survey aims to summarize the existing literature on DNA-based security techniques. It highlights various methods that use DNA sequences to protect confidential data, either during transmission or in storage and identifies the strengths and weaknesses of these methods, providing a comprehensive overview for researchers to assist in planning and designing secure techniques based on bioinformatics methods. By understanding the existing approaches and their limitations, researchers can develop more efficient and robust security techniques that leverage the power of DNA sequences and bioinformatics. This survey serves as a valuable resource for researchers seeking to explore and develop secure methods based on the intersection of bioinformatics and cryptography.

We describe a new low-level scheme to achieve high definition 3D-stereoscopy within the bandwidth of the monoscopic HDTV infrastructure. Our method uses a studio quality monoscopic high resolution color camera to generate a transmitted "main stream" view, and a flanking 3D-stereoscopic pair of low cost, low resolution monochrome camera "outriggers" to generate a depth map of the scene. The depth map is deeply compressed and transmitted as a low bandwidth "auxiliary stream". The two streams are recombined at the receiver to generate a 3D-stereoscopic pair of high resolution color views from the perspectives of the original outriggers. Alternatively, views from two arbitrary perspectives between (and, to a limited extent, beyond) the low resolution monoscopic camera positions can be synthesized to accommodate individual viewer preferences. We describe our algorithms, and the design and outcome of initial experiments. The experiments begin with three NTSC color images, degrade the outer pair to low resolution monochrome, and compare the results of coding and reconstruction to the originals.

In most digital cameras, color images are captured by a sensor overlaid by the Bayer color filter array (CFA). Denoisaicking (joint demosaicking and denoising) consists in reconstructing a color image from the noisy "Bayerized" data output by the sensor. We show that the frequency analysis of the sampling pattern induced by the Bayer CFA provides a simple way to reconstruct the luminance and chrominance channels of the image. The process is reduced to adequate linear filtering operations and denoising of the grayscale luminance image.

Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market and little attention has been directed to the design of alternative CFAs in the literature. Recent works of Hirakawa et al. [1]-[4] introduced new insights in this field by focusing on the spectral properties of CFAs and operating directly in the Fourier domain. However, this new paradigm is generic and leaves open questions about the optimization of the many available parameters. In this work, we investigate the link between them and the light sensitivity and color discrimination capabilities of the CFA. Indeed, these characteristics determine the quality of the whole imaging pipeline, since they directly control its sensitivity to noise. By optimizing the key parameters, we obtain a class of new CFAs with optimal properties, in which the shortest 2×3 pattern shows up as the best compromise. Moreover, a simple and efficient linear demosaicking algorithm is associated to these CFAs, which fully exploits their spectral properties. Practical experiments confirm the superiority of our new design.

Nous mettons en lumière certaines propriétés nouvelles du dématriçage linéaire par sélection spectrale. Ainsi, cette approche, simple et rapide, fournit une solution directe au problème du dématriçage par minimisation d'un critère quadratique variationnel. La linéarité de la méthode la rend robuste dans les conditions réelles d'aliasage et de bruit, une qualité que l'on ne retrouve pas chez les méthodes non linéaires de dématriçage de la littérature.

Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market and little attention has been directed to the design of alternative CFAs in the literature. Recent works of Hirakawa et al. [1]-[4] introduced new insights in this field by focusing on the spectral properties of CFAs and operating directly in the Fourier domain. However, this new paradigm is generic and leaves open questions about the optimization of the many available parameters. In this work, we investigate the link between them and the light sensitivity and color discrimination capabilities of the CFA. Indeed, these characteristics determine the quality of the whole imaging pipeline, since they directly control its sensitivity to noise. By optimizing the key parameters, we obtain a class of new CFAs with optimal properties, in which the shortest 2×3 pattern shows up as the best compromise. Moreover, a simple and efficient linear demosaicking algorithm is associated to these CFAs, which fully exploits their spectral properties. Practical experiments confirm the superiority of our new design.

Nous mettons en lumière certaines propriétés nouvelles du dématriçage linéaire par sélection spectrale. Ainsi, cette approche, simple et rapide, fournit une solution directe au problème du dématriçage par minimisation d'un critère quadratique variationnel. La linéarité de la méthode la rend robuste dans les conditions réelles d'aliasage et de bruit, une qualité que l'on ne retrouve pas chez les méthodes non linéaires de dématriçage de la littérature.

Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market, but there has been recently a renewed interest for the design of CFAs [2]-[6]. However, robustness to noise is often neglected in the design, though it is crucial in practice. In this work, we present a new 2 × 3-periodic CFA which provides, by construction, the optimal tradeoff between robustness to aliasing, chrominance noise and luminance noise. Moreover, a simple and efficient linear demosaicking algorithm is described, which fully exploits the spectral properties of the CFA. Practical experiments confirm the superiority of our design, both in noiseless and noisy scenarios.

We propose an algorithm that enhances the pixel number in high-speed camera image acquisition. In high-speed cameras, there is a principle problem that the number of pixels reduces when the number of frames per second (FPS) increases. To suppress this problem, we first propose an optical setup that randomly selects some percent of pixels in an image. Then, the proposed algorithm reconstructs the entire image from the selected partial pixels. In this algorithm, we exploit not only sparsity within each frame but also sparsity induced from the similarity between adjacent frames. Based on the two types of sparsity, we define a cost function for image reconstruction. Since this function is convex, we can find the optimal solution by using a convex optimization technique, in particular the Douglas-Rachford Splitting method, with small computational cost. Simulation results show that the proposed method outperforms a conventional method for sequential image reconstruction with sparsity prior.

A multiple color-filter aperture (MCA) camera can provide depth information as well as color and intensity in the single-camera framework, where the MCA generates misalignment between color channels depending on the distance of a region-of-interest. In this paper, we present a single camera-based estimation of the full depth map using the color shifting property of the MCA. For estimating the color shifting vectors (CSVs) among red, green, and blue color channels, edges are extracted at each color channel. At the edge, we estimate CSVs using normalized cross correlation combined with color shifting mask map. A full depth map is then generated by depth interpolation using the matting Laplacian method from sparsely estimated CSVs at an edge location. Experimental results show that the proposed method can not only estimate the full depth map but also correct the misaligned color image to generate photorealistic color images using a single camera equipped with MCA.

Image classification is one disposal procedure for high level semantic analysis, e.g., image data mining, knowledge discovery and intelligent data analysis. As one image generally contains multiple semantic objects, it's not appropriate to conduct image classification according the global features. So, image segmentation is a must before image classification. However, accurate semantic segmentation is still out of implicit semantic object regions in one image, as one semantic object with distinct regions would be segmented into several irrelevant regions by current most image segmentation algorithms based low level color or texture features, i.e., the hierarchical clustering based approach (HCBA), the eigenregions based strategy (EBS), and the novel region based methods (NRBM). This leads to the effectivity lose for these region-based image classification schemes, as a larger number of implicit regions will be achieved. Meanwhile, these individual regions are generally meaningful and unexpected results would be caused by these classification strategies, although some salient regions are extracted to reduce the complexity of region-based image classifications. Furthermore, these schemes are the arbitrary classification schemes, and could not indicate the information about other implicit objects. In this paper, we proposed salient region based fuzzy classification (SrFC) methods. Firstly, we give a salient region extraction algorithm based dominant colors and Gabor texture features. Then, according to the proposed classification scheme the salient regions from all the candidate images are clustered into disjoint categories. These categories are further used as for those for candidate images, and for each candidate image its number of salient regions belonging to the same category would be used to evaluate the degree of the images belonging to the category. Experiments showed the propose SrFC scheme can achieve better performance than that of current common schemes. With comparison to the HCBA scheme for individual salient regions, the classification accuracy is improved about 16% by the proposed SrFC scheme.

Building extraction and delineation is one of the most salient problems in cartographic features extraction. This paper presents a novel framework for reliable and accurate building extraction from high-resolution color imagery focusing on building boundary delineation and building roof compositional polygon segmentation. Proposed framework consists of three steps. First, anisotropic diffusion and clustering are applied as pre-processing for denoising and color quantization and then building boundary is extracted by active contour driven by edge-flow. Finally, building roof compositional polygons are segmented by JSEG. The framework is tested on a number of buildings and the results are shown. The result shows the completeness and accuracy that this framework can provide for extracting building from a high-resolution color image data set.

Ensuring color fidelity in image-based 3D modeling of heritage scenarios is nowadays still an open research matter. Image colors are important during the data processing as they affect algorithm outcomes, therefore their correct treatment, reduction and enhancement is fundamental. In this contribution, we present an automated solution developed to improve the radiometric quality of an image datasets and the performances of two main steps of the photogrammetric pipeline (camera orientation and dense image matching). The suggested solution aims to achieve a robust automatic color balance and exposure equalization, stability of the RGB-to-gray image conversion and faithful color appearance of a digitized artifact. The innovative aspects of the article are: complete automation, better color target detection, a MATLAB implementation of the ACR scripts created by Fraser and the use of a specific weighted polynomial regression. A series of tests are presented to demonstrate the efficiency ...

In this paper, two image encryption schemes are proposed for grayscale and color images. The two encryption schemes are based on dividing each image into blocks of different sizes. In the first scheme, the two dimension (2D) input image is divided into various blocks of size N × N . Each block is transformed into a one dimensional (1D) array by using the Zigzag pattern. Then, the exclusive or (XOR) logical operation is used to encrypt each block with the analogous secret key. In the second scheme, after the transformation process, the first block of each image is encrypted by the corresponding secret key. Then, before the next block is encrypted, it is XORed with the first encrypted block to become the next input to the encrypting routine and so on. This feedback mechanism depends on the cipher block chaining (CBC) mode of operation which considers the heart of some ciphers because it is highly nonlinear. In the case of color images, the color component is separated into blocks with the same size and different secret keys. The used secret key sequences are generated from elliptic curves (EC) over a binary finite field F2m . Finally, the experimental results are carried out and security analysis of the ciphered images are demonstrated that the two proposed schemes had a better performance in terms of security, sensitivity and robustness.

This study proposes a new approach to enhance the dark and lowcontrast satellite images captured under poor illumination. Images are converted from RGB to HSV color space in order to separate the Hue (H), Saturation (S), and luminance (V) components. Enhancement is achieved in saturation and luminance components using Discrete Wavelet Transform (DWT) and 2-type fuzzy logic. DWT breaks the images into high and lowfrequency sub-bands and results in approximation and detailed coefficients. The approximation coefficient consists of low-frequency components, which are further modified using the 2-type Fuzzy Logic technique. Fuzzy logic is basically a point operation-based technique based on the fact that human perceived brightness characteristic is not linear. Inverse Wavelet Transform is used to achieve the enhanced saturation components. The luminance component (V) is passed through class-limited adaptive histogram equalization. All the enhanced components are then combined to get the enhanced image and finally reconverted to RGB color space. The proposed scheme significantly works well for enhancing and sharpening dark and low-contrast satellite images. The experimental results obtained by the proposed method show improved performance in terms of PSNR, MSE, mean, and variance.