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Demosaicking is a method to restore the mosaicked image generated by the color filter array to a full-color image. The performance of the demosaicking algorithm is crucial to the reconstruction of high-quality color images. However, current demosaicking algorithms inevitably cause zipper effects or false color artifacts in the process of demosaicking. We combine low-rank constraint based on spectral domain with image non-local similarity to correct the interpolation errors. Our method is applicable to various existing demosaicking algorithms and can further improve the quality of full color image reconstruction. Experimental results demonstrate that the proposed algorithm can improve the color peak signal to noise ratio of the reconstructed images.

Image demosaicking is a method of reconstructing an RGB image from a Bayer pattern, which is required when color information is lacking because a single charge-coupled device is used during the image extraction process of a digital camera. There are many restrictions on the reconstruction of a Bayer image to an RGB image. Given that each pixel contains only one-color information, artifacts, such as false color or the zipper effect, may occur at the edges, which can arise as a result of significant differences in brightness and color change. We propose a demosaicking method for adaptively selecting the reference range of color difference to obtain reliable information from texture regions and reconstructing into the RGB image. In particular, we determine the adaptive weight and reference range for four directions, east (E), west (W), south (S), and north (N), to improve the reliability of the color pixel obtained by a color difference estimation using guided filtering applied on residuals. In our experiment, we compare the results of the proposed method for the Kodak and IMAX datasets with those of nine demosaicking methods. The proposed method shows similar or improved results in terms of the color peak signal-to-noise ratio. In addition, compared to other methods, the visual quality improved by reducing residual artifacts.

Color demosaicing is a key image processing step aiming to reconstruct the missing pixels from a recorded raw image that has a color filter array (CFA) pattern. The color correlation–based guided filters, such as minimized-Laplacian residual interpolation (MLRI), are known as the state-of-the-art demosaicing techniques. However, in the conventional guided filter-based techniques, the artifacts are generated in areas with low color correlation. Furthermore, a large number of line memories are required in the hardware implementation of a conventional guided filter-based technique because of the large effective field size. To overcome these two problems, we propose a color correlation–adaptive demosaicing algorithm that selectively applies a specific intracolor demosaicing to regions with low color correlation. We also propose an algorithm structure that reduces the effective field size in the vertical direction to reduce the number of line memories, while maintaining the image quality performance during the hardware implementation. The experimental results show that the proposed scheme can reduce the line memory to one-third while showing marginal performance degradation compared to the state-of-the-art MLRI weighted framework in terms of the color peak signal-to-noise ratio for the IMAX datasets.

Image demosaicking is the problem of reconstructing color images from raw images captured using a digital camera with a color filter array. Sparse representation-based demosaicking method achieves superior performance on the commonly used Kodak dataset; however, it performs inferior on the IMAX dataset. We analyze that the factor of the sparse representation-based demosaicking methods that perform inconsistently is channel-correlation, which we define as the mean value of correlation coefficients between the RGB channels. Accordingly, we propose a channel-correlation adaptive dictionary learning-based demosaicking method. Different from the sparse representation-based demosaicking methods that use a fixed dictionary, our method trains a general dictionary on training image patches with various channel-correlations. Then, we learn a function matrix between the general dictionary and channel-correlations. For a raw image patch with an estimated channel-correlation, we compute a dictionary corresponding to its channel-correlation through the function matrix. Finally, we demosaick it with the corresponding dictionary using the sparse representation model. Experiments confirm that the proposed method performs adaptively well on raw images with various channel-correlations.

This study presents a Bayesian approach based on a color image demosaicking algorithm. The proposed method is composed of pointwise and patchwise measurements. The estimation of the missing pixel is formulated as a maximum a posteriori and a minimum energy function. By utilizing Bayesian theory and some prior knowledge, the missing color information is estimated with a statistics-based approach. Under the maximum a posteriori and Bayesian framework, the desired target image corresponds to the optimal reconstruction given the mosaicked image. Compared with existing demosaicking methods, the proposed algorithm improves the CPSNR, S-CIELAB, FSIM, and zipper effect measurements while maintaining high efficiency. Moreover, it handles Gaussian and Poisson noisy images better than other conventional images.

We propose a method to enhance the color image of a low-light scene using a single sensor that simultaneously captures red, green, blue (RGB), and near-infrared (NIR) information. Typical image enhancement methods require two sensors to simultaneously capture color and NIR images. In contrast, our proposed system utilizes a single sensor but achieves accurate color image restoration. We divide the captured multispectral data into RGB and NIR information based on the spectral sensitivity of our imaging system. Using the NIR information for guidance, we reconstruct the corresponding color image based on a joint demosaicking and denoising technique. Subsequently, we restore the estimated color image iteratively using the constructed guidance image. Our experiments demonstrate the effectiveness of our method using synthetic data, and real raw data captured by our imaging system.

Endoscopy is an imaging procedure used for diagnosis as well as for some surgical purposes. The camera used for the endoscopy should be small and able to produce a good quality image or video, to reduce discomfort of the patients, and to increase the efficiency of the medical team. To achieve these fundamental goals, a small endoscopy camera with a footprint of 1 mm×1 mm×1.65 mm is used. Due to the physical properties of the sensors and human vision system limitations, different image-processing algorithms, such as noise reduction, demosaicking, and gamma correction, among others, are needed to faithfully reproduce the image or video. A full image-processing pipeline is implemented using a field-programmable gate array (FPGA) to accomplish a high frame rate of 60 fps with minimum processing delay. Along with this, a viewer has also been developed to display and control the image-processing pipeline. The control and data transfer are done by a USB 3.0 end point in the computer. The full developed system achieves real-time processing of the image and fits in a Xilinx Spartan-6LX150 FPGA.

We apply the Bayesian shrinkage dictionary learning into compressive dynamic-range imaging. By attenuating the luminous intensity impinging upon the detector at the pixel level, we demonstrate a conceptual design of an 8-bit camera to sample high-dynamic-range scenes with a single snapshot. Coding strategies for both monochrome and color cameras are proposed. A Bayesian reconstruction algorithm is developed to learn a dictionary in situ on the sampled image, for joint reconstruction and demosaicking. We use global-local shrinkage priors to learn the dictionary and dictionary coefficients representing the data. Simulation results demonstrate the feasibility of the proposed camera and the superior performance of the Bayesian shrinkage dictionary learning algorithm.

A set of invariant quaternion moments based on an adaptation of the three-dimensional (3-D) spherical harmonic transform (SHT) for describing two-dimensional color shapes is proposed. The use of quaternions to deal with the color part is beneficial in the way the three color components are integrated in a single feature. An adequate mapping from the 3-D SHT to the unit disc allows a fast and accurate computation of the proposed moments. Experiments are conducted to evaluate the performance of the obtained moments in terms of color image reconstruction, robustness to geometric and photometric transformations, content-based color shape retrieval, and computation time. For this purpose, two image databases (COIL-100 and ALOI) are used. Results illustrate the effectiveness of the proposed moments in dealing with the color information.

A near-infrared (NIR) band provides information invisible to human eyes for discriminating and recognizing objects more clearly under low lighting conditions. To capture color and NIR images simultaneously, a multispectral filter array (MSFA) sensor is used. However, because lenses have different refractive indices for different wavelengths, lenses may fail to focus all rays to the same convergence. This is the reason an out-of-focus problem occurs and images are blurred. In this paper, a demosaicking algorithm that considers the out-of-focus problem is proposed. This algorithm is used by the MSFA of a red-green-blue-NIR image sensor to obtain color and NIR images. After the energies of the multispectral (MS) channels in the MSFA image are balanced to minimize aliasing, that image is filtered by the estimated low-pass kernel to generate a panchromatic (PAN) image. When an image is acquired, the out-of-focus problem and the formation process of the PAN image are modeled. The desired MS image is estimated by solving the least squares approach of the difference between the PAN and MS images based on the models. The experimental results show that the proposed algorithm performs well in estimating high-quality MS images and reduces the out-of-focus problem.

A single sensor camera can capture scenes by means of a color filter array. Each pixel samples only one of the three primary colors. We use a color demosaicking (CDM) technique to produce full color images and propose a robust adaptive sparse representation model for high quality CDM. The data fidelity term is characterized by l 1 norm to suppress the heavy-tailed visual artifacts with an adaptively learned dictionary, while the regularization term is encouraged to seek sparsity by forcing sparse coding close to its nonlocal means to reduce coding errors. Based on the classical quadratic penalty function technique in optimization and an operator splitting method in convex analysis, we further present an effective iterative algorithm to solve the variational problem. The efficiency of the proposed method is demonstrated by experimental results with simulated and real camera data.

We present an improved directional weighted interpolation method for single-sensor camera imaging. By observing the fact that the conventional directional weighted interpolation methods are based on unreliable assumptions using spectral correlation, a contribution of this work is made using an antialiasing finite impulse response filter to improve the interpolation accuracy by exploiting robust spectral correlation. We also make a contribution toward refining the interpolation result by using the gradient inverse weighted filtering method. An experimental analysis of images revealed that our proposed algorithm provided superior performance in terms of both objective and subjective image quality compared to conventional directional weighted demosaicking algorithms. Our implementation has very low complexity and is, therefore, well suited for real-time applications.

The recently published joint demosaicking and zooming algorithms for single-sensor digital cameras all overfit the popular Kodak test images, which have been found to have higher spectral correlation than typical color images. Their performance perhaps significantly degrades on other datasets, such as the McMaster test images, which have weak spectral correlation. A new joint demosaicking and zooming algorithm is proposed for the Bayer color filter array (CFA) pattern, in which the edge direction information (edge map) extracted from the raw CFA data is consistently used in demosaicking and zooming. It also moderately utilizes the spectral corre- lation between color planes. The experimental results confirm that the proposed algorithm produces an excellent performance on both the Kodak and McMaster datasets in terms of both subjective and objective measures. Our algorithm also has high computational efficiency. It provides a better tradeoff among adaptability, performance, and computational cost compared to the existing algorithms. © 2014 SPIE and IS&T (DOI: 10.1117/1.JEI.23.4.043010)

Recently, color filter array patterns based on optimal design by reducing interchannel aliasing have been developed. Although these optimally subsampled patterns produce less degradation of resolution for the images in daylight about 6500 K, aliasing and grid effect become more prominent under the different lighting conditions. Moreover, the color interpolation (CI) for Bayer pattern cannot be utilized for these patterns. Since it is impossible to change coated patterns according to various conditions, CI algorithms have to compensate for light variations. In this letter, a method for generating intermediate quincuncial pattern is proposed for two patterns. Also, edge adaptive interpolation for the quincuncial pattern is carried out. Experimental results show that the proposed method reduces aliasing and grid effect.

We propose a local region statistics-based weighted interpolation filter for intrafield deinterlacing. The proposed algorithm consists of three steps: first, we preinterpolate the missing line with an efficient interpolation filter in the working window. Then, we calculate the coefficients of the center-weighted interpolation filter by exploiting local statistics, namely the center-weighted mean, the center-weighted variance, and the closeness of the neighboring pixels. In the last step, we interpolate the missing line using the proposed filter. Experimental results show that the proposed algorithm provides superior performances in terms of both objective and subjective image qualities than the existing algorithm.

Computationally scalable image interpolation algorithm is always desirable for software and hardware implementations on center processing unit (CPU), digital signal processor, field-programmable gate array, and low-cost hardware. A low-complexity, computationally scalable, and data-adaptive image interpolation algorithm that has a simple and homogeneous structure to efficiently scale the computation is proposed. Specifically, the image interpolation as a denoising problem is formulated by proposing a new image model to relate the observed low-resolution pixels and missing high-resolution pixels. Applying the maximum-likelihood estimation using the new image model results in an adaptive linear filter, where the filter coefficients depend on the local noise covariance matrix, which is estimated by local noise samples. Due to low overhead of the proposed interpolator, the overall computation efficiently scales with the number of noise samples. Experimental results show that the proposed scalable algorithm outperforms the state-of-the-art fast algorithms and achieves more than 36 frames per second for upscaling a 540 p (960×540 ) video to a 1080 p (1920×1080 ) video using multithreaded C++ software implementation on a PC system with Intel i7 950 3 GHz CPU.

The goal of this paper is to simulate the functionality of a digital camera system. The simulations cover the conversion from light to numerical signal, color processing, and rendering. A spectral image processing algorithm is used to simulate the radiometric properties of a digital camera. In the algorithm, we take into consideration the spectral image and the transmittances of the light source, lenses, filters, and the quantum efficiency of a complementary metal-oxide semiconductor (CMOS) image sensor. The optical part is characterized by a multiple convolution between the different point spread functions optical components such as the Cooke triplet, the aperture, the light fall off, and the optical part of the CMOS sensor. The electrical part consists of the Bayer sampling, interpolation, dynamic range, and analog to digital conversion. The reconstruction of the noisy blurred image is performed by blending different light exposed images in order to reduce the noise. Then, the image is filtered, deconvoluted, and sharpened to eliminate the noise and blur. Next, we have the color processing and rendering blocks interpolation, white balancing, color correction, conversion from XYZ color space to LAB color space, and, then, into the RGB color space, the color saturation and contrast.

Interpolation is an essential and broadly employed function of signal processing. Accordingly, considerable development has focused on advancing interpolation algorithms toward optimal accuracy. Such development has motivated a clear shift in the state-of-the art from classical interpolation to more intelligent and resourceful approaches, registration-based interpolation for example. As a natural result, many of the most accurate current algorithms are highly complex, specific, and computationally demanding. However, the diverse hardware destinations for interpolation algorithms present unique constraints that often preclude use of the most accurate available options. For example, while computationally demanding interpolators may be suitable for highly equipped image processing platforms (e.g., computer workstations and clusters), only more efficient interpolators may be practical for less well equipped platforms (e.g., smartphones and tablet computers). The latter examples of consumer electronics present a design tradeoff in this regard: high accuracy interpolation benefits the consumer experience but computing capabilities are limited. It follows that interpolators with favorable combinations of accuracy and efficiency are of great practical value to the consumer electronics industry. We address multidimensional interpolation-based image processing problems that are common to consumer electronic devices through a decomposition approach. The multidimensional problems are first broken down into multiple, independent, one-dimensional (1-D) interpolation steps that are then executed with a newly modified registration-based one-dimensional control grid interpolator. The proposed approach, decomposed multidimensional control grid interpolation (DMCGI), combines the accuracy of registration-based interpolation with the simplicity, flexibility, and computational efficiency of a 1-D interpolation framework. Results demonstrate that DMCGI provides improved interpolation accuracy (and other benefits) in image resizing, color sample demosaicing, and video deinterlacing applications, at a computational cost that is manageable or reduced in comparison to popular alternatives.

Bidimensional empirical mode decomposition (BEMD) technique decomposes an image into several bidimensional intrinsic mode functions (BIMFs) and a bidimensional residue (BR). Classical BEMD methods require some form of surface interpolation to estimate envelope surfaces, which causes various problems. On the other hand, existing surface interpolation-based BEMD methods are proposed and/or are suitable for gray-scale images only. This paper presents a novel BEMD approach for color images known as color BEMD (CBEMD), which employs order-statistics filter (OSF)-based envelope estimation to avoid some of the difficulties, otherwise encountered in classical BEMD approaches. The CBEMD can decompose a color image into several color BIMFs and a color BR based on hierarchical local spatial variation of image intensity and color. Since the color BR represents the trend of a color image in terms of global intensity and color distribution, it is utilized for trend adjustment of color images. Both formulation of the CBEMD and finding and adjusting the color image trend are known to be the first approaches in corresponding issues. Experimental results with several real images demonstrate the potential of the proposed CBEMD method for color image processing, which include trend adjustment of color images.

A fast pixel-size-based very large-scale enlargement and reduction of image via an adaptive combination of bilinear interpolation and discrete cosine transform (DCT) is proposed to improve the quality and speed of the image zooming process. The proposed algorithm combined with DCT and bilinear interpolation is proposed to adaptively process different blocks of an image with different characteristics for a very large-scale image enlargement and reduction. Moreover, if quality is the main concern factor, the evolutionary programming is applied to further improve the quality of reduction algorithm of an image by tuning the frequency coefficient matrix of the reduced image. Finally, for dealing with arbitrary pixel-size-based zooming of an image, a new algorithm is also proposed.

An interpolation method for a line-pruned image is presented. The goal is to alleviate the time complexity of the previous NEDI-8 (new edge directed interpolation) method. The idea of our solution is to use NEDI-4 selectively, applying the NEDI-4 for edge pixels while using a linear filter for others. For the linear filter, the interpolation coefficients for each pruned line are adaptively determined.

Image interpolation addresses the problem of obtaining high resolution (HR) images from its low resolution (LR) counterparts. For observed LR images with aliasing artifacts caused by undersampling, commonly used interpolation methods cannot recover HR images well, and may often interpolate over-fitting artifacts. In this paper, based on the observation that natural images normally have redundant similar patches, a new patch-synthesis-based interpolation method is proposed for image interpolation. In the proposed method, an inference method based on Markov chain is adopted to select the best patches from the input LR image and synthesize them into the undersampled areas of a desired HR image. In order to improve the efficiency of the algorithm, we also introduce fields of experts to model the sparse prior knowledge and use it to measure the compatibilities among neighboring patches. Experimental results compared with traditional interpolation methods demonstrate that our method cannot only alleviate the aliasing artifact, but also produce better results in terms of quantitative evaluation and subjective visual quality

This paper proposes a new regularization scheme to address blind color image deconvolution. Color images generally have a significant correlation among the red, green, and blue channels. Conventional blind monochromatic deconvolution algorithms handle each color image channels independently, thereby ignoring the interchannel correlation present in the color images. In view of this, a unified regularization scheme for image is developed to recover edges of color images and reduce color artifacts. In addition, by using the color image properties, a spectral-based regularization operator is adopted to impose constraints on the blurs. Further, this paper proposes a reinforcement regularization framework that integrates a soft parametric learning term in addressing blind color image deconvolution. A blur modeling scheme is developed to evaluate the relevance of manifold parametric blur structures, and the information is integrated into the deconvolution scheme. An optimization procedure called alternating minimization is then employed to iteratively minimize the image- and blur-domain cost functions. Experimental results show that the method is able to achieve satisfactory restored color images under different blurring conditions.

Color demosaicking is critical to the image quality of single-sensor-based imaging devices. Caused by the sampling pattern of color filter array (CFA), the demosaicked images typically suffer from visual color artifacts in regions of high frequency and sharp edge structures, degrading the quality of camera output. We present a new high-quality demosaicking algorithm by taking advantage of deinterlacing and median-based filtering techniques. We treat the sampled green data of Bayer CFA as a form of diagonal interlaced green planes and make use of some key concepts about spatial deinterlacing to help the edge estimation in terms of both various directions and accuracy. In addition, a specific edge feature, sharp line edge of width 1 pixel, can also be handed well by the proposed method. The median-based filtering techniques are developed for suppressing most visual demosaicking artifacts, such as zipper effect, false color artifact, and interpolation artifact. Experimental results show that our algorithm is effective in suppressing visual artifacts, preserving the edges of image with sharpness and satisfying visual inspection, while keeping computational efficiency.

We present a field-programmable gate array (FPGA)-based hardware architecture for image processing as well as novel algorithms for fast autoexposure control and color filter array (CFA) demosaicing utilizing a CMOS image sensor (CIS). The proposed hardware architecture includes basic color processing functions of black-level correction, noise reduction, autoexposure control, auto-white-balance adjustment, CFA demosaicing, and gamma correction while applying advanced peripheral bus architecture to implement the hardware architecture. The speed of traditional autoexposure control algorithms to reach a proper exposure level is so slow that it is necessary to develop a fast autoexposure control method. Based on the optical-electrical characteristics of the CIS, we present a fast auto-exposure-control algorithm that can guarantee speed and accuracy. To ensure the peak SNR performance of the demosaiced images of the CIS and reduce the computational cost at the same time, the proposed demosaicing algorithm improves on the adaptive edge-sensitive algorithm and the fuzzy assignment algorithm. The experimental results show that the proposed hardware architecture works well on the FPGA development board and produces better quality images.

Color interpolation solutions drastically influence the quality of the whole image generation pipeline, so they must guar- antee the rendering of high quality pictures by avoiding typical arti- facts such as blurring, zipper effects, and false colors. Moreover, demosaicing should avoid emphasizing typical artifacts of real sen- sors data, such as noise and green imbalance effect, which would be further accentuated by the subsequent steps of the processing pipeline. We propose a new adaptive algorithm that decides the interpolation technique to apply to each pixel, according to its neigh- borhood analysis. Edges are effectively interpolated through a direc- tional filtering approach that interpolates the missing colors, select- ing the suitable filter depending on edge orientation. Regions close to edges are interpolated through a simpler demosaicing approach. Thus flat regions are identified and low-pass filtered to eliminate some residual noise and to minimize the annoying green imbalance effect. Finally, an effective false color removal algorithm is used as a postprocessing step to eliminate residual color errors. The experi- mental results show how sharp edges are preserved, whereas un- desired zipper effects are reduced, improving the edge resolution itself and obtaining superior image quality. © 2010 SPIE and

Color demosaicing is critical for digital cameras, because it converts a Bayer sensor mosaic output to a full color image, which determines the output image quality of the camera. In this work, an efficient decision-based demosaicing method is presented. This method exploits a new edge-sensing measure called integrated gradient (IG) to effectively extract gradient information in both color intensity and color difference domains simultaneously. This measure is reliable and supports full resolution, which allows one to interpolate the missing samples along an appropriate direction and hence directly improves the demosaicing performance. By sharing it in different demosaicing stages to guide the interpolation of various color planes, it guarantees the consistency of the interpolation direction in different color channels and saves the effort required to repeatedly extract gradient information from intermediate interpolation results at different stages. An IG-based green plane enhancement is also proposed to further improve the method's efficiency. Simulation results confirm that the proposed demosaicing method outperforms up-to-date demosaicing methods in terms of output quality at a complexity of around 80 arithmetic operations per pixel.

We propose a modification to the standard Bayer CFA and photodiode structure for CMOS image sensors, which we call 2PFC (Two Pixels, Full Color). The blue and red filters of the Bayer pattern are replaced by a magenta filter. Under each magenta filter are two stacked, pinned photodiodes; the diode nearest the surface absorbs mostly blue light and the deeper diode absorbs mostly red light. The magenta filter absorbs green light, improving color separation between the blue and red diodes. We first present a frequency-based demosaicing method, which takes advantage of the new 2PFC geometry. Due to the spatial arrangement of red, green, and blue pixels, luminance and chrominance are very well separated in the Fourier space, allowing for computationally inexpensive linear filtering. In comparison with state-of-the-art demosaicing methods for the Bayer CFA, we show that our sensor and demosaicing method outperform the others in terms of color aliasing, PSNR, and zipper effect. As demosaicing alone does not determine image quality, we also analyze the whole system performance in terms of resolution and noise.

Demosaicking is an interpolation process that transforms a color filter array (CFA) image into a full-color image in a single-sensor imaging pipeline. In all demosaicking techniques, the interpolation of the green components plays a central role in dictating the visual quality of reconstructed images because green light is of maximum sensitivity in the human visual system. Guided by this point, we propose a new soft-decision demosaicking algorithm using directional filtering and embedded artifact refinement. The novelty of this approach is twofold. First, we lift the constraint of the Bayer CFA that results in the absence of diagonal neighboring green color values for directionally recovering diagonal edges. The developed directional interpolation method is fairly robust in dealing with the four edge features, namely, vertical, horizontal, 45-deg diagonal, and 135-deg diagonal. In addition, the proposed embedded refinement scheme provides an efficient way for soft-decision-based algorithms to achieve improved results with fewer computations. We have compared this new approach to six state-of-the-art methods, and it can outstandingly preserve more edge details and handle fine textures well, without requiring a high computational cost.

A system for simultaneous real-time three-dimensional geometry and color texture acquisition. The system includes a system processor for generating at least three phase shifted black and white fringe patterns with a phase shift of 2 π/3, a light projector adapted to project the fringe patterns onto an object, the projector being electrically connected with the system processor, and a color camera for capturing the fringe patterns to generate at least three raw fringe images. The fringe images are used to calculate a black and white texture image which is further converted to a color image by employing a demosaicing algorithm. The fringe images are also used to calculate a wrapped phase map that is further processed to generate a continuous unwrapped phase map by employing a phase unwrapping algorithm and the unwrapped phase map is converted to co-ordinates using calibrated system parameters for point-by-point three-dimensional shape measurement.

The light information obtained through a color filter array is sampled as R (red), G (green), and B (blue) based on the intensity of the radiation sensor CCD charge-coupled device (CCD) or complementary metal oxide semiconductor (CMOS). Because one pixel in the CCD or CMOS has a wave value for only one color, the information on the other two colors has to be estimated from the samples of the neighboring pixels. This estimation process is called demosaicking, and it plays an important role in the image pipeline. We propose a demosaicking method that uses the correlation among the wavelet coefficients in the high-frequency region of the color channels (R, G, and B). In our method, the high-frequency wavelet coefficients are obtained by linear estimation. The low-frequency coefficients are replaced by the results obtained from bilinear interpolation. We simulate our method and compare it with existing demosaicking schemes. The experimental results show that the proposed method can perform good demosaicking.

A new method for color image segmentation is proposed. It is based on a novel region-growing technique with a growth tolerance parameter that changes with step size, which depends on the variance of the actual grown region. Contrast is introduced to determine which value of the tolerance parameter is taken, choosing the one that provides the region with the highest contrast in relation to the background. Color and texture information are extracted from the image by means of a novel idea: the construction of a color distance image and a texture energy image. The color distance image is formed by calculating CIEDE2000 distance in the L*a*b* color space. The texture energy image is extracted from some statistical moments. Then, a novel texture-controlled multistep region-growing process is performed for the segmentation. One advantage of the method is that it is not designed to work with a particular kind of images. This method is tested on 80 natural color images of the Corel photo stock collection with excellent results. Numerical evidence of the quality of these results is provided by comparing them with the manual segmentation of five experts and with another color and texture segmentation algorithm.

Color filter array (CFA) demosaicking is an essential process for restoring full-color images from incomplete color samples acquired by single-sensor digital cameras. We present two main contributions to CFA demosaicking. First, we analyze the causes of two main types of CFA demosaicking artifacts and examine the schemes that are effective in suppressing them, respectively. Second, by combining and extending the core merits of the schemes examined, we compose a new CFA demosaicking algorithm to suppress as many demosaicking artifacts as possible and obtain full-color images of high quality. Experiments using a large variety of test images have been conducted, and the results show that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio.

The problem of recovering full-color images from a noisy and color-sampled observation is considered. The noise in imaging sensors corrupts the color filter array (CFA) and introduces artifacts during the color interpolation step. Instead of filtering the noise before or after the color interpolation step, we remove the noise during the color interpolation step. This allows us to obtain a color interpolated image without noise and artifacts, especially along the edges and in the detail of the image. Color interpolation is carried out on the weighted color difference domain to consider cross-channel correlation. To avoid artifacts in high frequency regions and improve the performance, an edge indicator function is used and directions of edges are considered in the proposed interpolation method, while noise is simultaneously removed by adaptive noise filtering. As postprocessing, modified filtering on the color difference domain is adopted to improve the quality of the image. Experimental results illustrate the benefits of the proposed method. When compared to conventional methods, the proposed method outperforms them on quantitative and qualitative criteria.

We develop a digital camera model with the purpose of simulating the performance of a sky radiance colorimeter, based on a single-sensor camera equipped with a charge-coupled device imager. To provide a color image, the sensor is covered with a Bayer color filter array (CFA). The model input is the radiance, expressed in radiometric units. Demosaicking of the CFA is performed using three common interpolation methods. Color characterization is performed by calculating 3×3 matrices, based on either the maximum-ignorance assumption or computer-generated sky radiance training sets. For the model to be reliable, we incorporate multiple noise sources. We also parameterize many "critical" characteristics of the simulated imager, for example, temperature-related dark-current generation and pixel size. Error propagation analysis is performed, including an analysis for the demosaicking methods. Colorimetric performance results, expected measurement accuracy, and validation of the derived noise propagation equations are presented.

We propose a novel demosaicking method based on the linearity property of a local color distribution. With the proposed technique, the color filter array can be demosaicked with less "confetti" types of errors and fringe artifacts than many current demosaicking methods. Furthermore, edge details are well preserved.

A new adaptive filter is presented, designed for the restora- tion of color images corrupted with an arbitrary noise model. The pro- posed filter is based on order-statistics restoration techniques and per- forms a linear combination of a number of difference measures to estimate the noise present in each pixel. To reduce color artifacts, the filtering is performed in a color space that decouples the intensity and color information. The choice of which color space is arbitrary; however, from experimentation it has been found that a spherical coordinate rep- resentation of the RGB pixel vectors provides the best results. As the results show, the proposed filter outperforms the current state of the art techniques for impulse, additive, and multiplicative noise types. In addi- tion to the proposed filter, a new color performance measure is intro- duced based on the mean square error; however, it can be calculated using any error measure that employs differences between two input signals. © 2005 Society of Photo-Optical Instrumentation Engineers.

A class of partition-based interpolators that addresses a vari- ety of image interpolation applications are proposed. The proposed in- terpolators first partition an image into a finite set of partitions that cap- ture local image structures. Missing high resolution pixels are then obtained through linear operations on neighboring pixels that exploit the captured image structure. By exploiting the local image structure, the proposed algorithm produces excellent performance on both edge and uniform regions. The presented results demonstrate that partition-based interpolation yields results superior to traditional and advanced algo- rithms in the applications of color filter array CFA demosaicking and super-resolution reconstruction. © 2005 Society of Photo-Optical Instrumentation

A new image hiding scheme is proposed that can embed color secret images into color host images. The secret image to be embedded is first color quantized using a palette previously sorted by the principal component analysis technique to generate the indexed color image. Then the concept of pixel difference is employed to process both the host image and the indexed color image. Then the difference image of the indexed color image is embedded into the difference image of the color host image. According to our experimental results, the image quality of the stegoimage is quite good when our new method is used to embed a color secret image into the color host image.

We propose rapid hybrid interpolation methods that employ more than one interpolation algorithm, and choose the most appropriate interpolation algorithm that provides high-quality images with a minimum number of operations. Although a complex interpolation algorithm generally outperforms a simple interpolation algorithm, the differences are negligible for most pixels, with major differences occurring around edges. Thus, in the proposed algorithm, we first apply a test to predict which interpolation is most appropriate for a given pixel in terms of complexity and performance. Then, a simple interpolation algorithm is used for pixels for which the simple interpolation algorithm provides acceptable performances, and a complex interpolation algorithm is used for pixels for which the complex interpolation algorithm significantly outperforms the simple interpolation algorithm. Consequently, it is possible to obtain high-quality images without significantly increasing the number of operations.

We describe a novel interpolation algorithm to find the optimal image intensity function generating an optimal gray-level estimation of interpolated pixels of digital images. The new approach is based on the proposed image block mapping method and least-square support vector machines (LSSVM) with Gaussian radial basis function (RBF) kernels. With the mapping technique, the interpolation procedure of the LSSVM is actually accomplished in the same input vector space. A number of different scale interpolation experiments are carried out. The experimental results demonstrate that the performance of the proposed algorithm is competitive with many other existing methods, such as cubic, spline, and linear methods. The peak signal-to-noise ratio of the image reconstructed by the proposed algorithm is higher than those obtained by the spline. And the estimated accuracy of the proposed algorithm is similar to that of the cubic algorithm, while the computational requirement is lower than the latter.

We present a novel method for color interpolation that is specially designed for color-filter-array images acquired with charge-coupled device (CCD) detectors. The interpolation technique combines edge direction weighting and the local gain approach to determine missing sensor information with high accuracy but low computational costs. Simulations demonstrate that the proposed algorithm can achieve high spatial resolution and color fidelity for camera imaging systems. The averaged peak signal-to-noise ratio (PSNR) is improved about 2.3, 1.1, and 0.4 dB compared to bilinear interpolation, cubic B-spline, and discriminated color correlation, respectively. Moreover, the new color interpolation uses low computational complexity in terms of the number of additions and multiplications to be performed, and it requires less memory than the cubic B-spline and discriminated color correlation methods.

A color interpolation algorithm is necessary to acquire color images of good quality with a digital still camera in which a single CCD image sensor is equiped. Since a color filter array is adopted, we must interpolate missing color values in the red, green, and blue channels at each pixel location. Inappropriate color interpolation causes color arti- facts, resulting in degraded color resolution. A color interpolation algo- rithm with a variable color ratio is proposed by incorporating cross- channel correlation. The color ratio of weighted sums in the color channels is utilized instead of the weighted sum of color ratios, and the color ratio varies with respect to the cross-channel correlation at each pixel. The proposed algorithm is composed of initial color interpolation step and the updating step of the green channel, where the updating step is carried out iteratively. Furthermore, a minus weighted sum based on the external divide, a modified edge indicator function, and a 5 35 support region for the red and blue channels are also adopted to improve the performance. With the proposed algorithm, we have achieved im- provements in the image quality especially along the edges and in the detail of the image by removing color artifacts. The superiority of the proposed algorithm is demonstrated in the experimental results. © 2004

We present a color filter array interpolation algorithm to provide the missing colors associated with the commonly used Bayer pattern in digital still camera systems. The developed algo- rithm utilizes color correlation and directional derivative attributes of neighboring color pixels and is thus named C2D2. The main advan- tage of this algorithm is that it requires no user-defined parameter, as is the case in many edge-adaptive color filter array interpolation algorithms. Both an objective and subjective comparison are carried out with two other popular edge-adaptive algorithms. The results indicate that this algorithm consistently generates lower mean- squared interpolation errors in the RGB and L*a*b* color spaces.

Single chip charge-coupled devices (CCDs) coupled with filters for isolating red, green, and blue color content are commonly used to capture color images. While this is more cost effective than multiple chip systems, best results are obtained when full RGB color information is obtained for every point in an image. The process of color subsampling in a single chip system degrades the resulting image data by introducing artifacts such as blurry edges and false coloring. We propose an algorithm for enhancing color image data that were captured with a typical single chip CCD array. The algo- rithm is based on stochastic regularization using a Markov random field model for the image data. This results in a constrained optimi- zation problem, which is solved using an iterative constrained gra- dient descent computational algorithm. Results of the proposed al- gorithm show a marked improvement over the original sampled image data. © 2003 SPIE and IS&T. (DOI: 10.1117/1.1560643)

We introduce the iteratively clustered interpolation algorithm to compute a structured printer inverse look-up table from irregularly sampled experimental color data. The algorithm is based on a gradient optimization method, with initial points generated through an iterative technique. Experimental results with a digital color printer are provided to illustrate the algorithm.

Yonsei UniversityDepartment of Electrical & ElectronicEngineering134 Shinchon, Seodaemun-KuSeoul, KoreaE-mail: mkang@yonsei.ac.krAbstract. The color interpolation scheme can play an important role inovercoming the physical limitation of CCD image sensors and in increas-ing the resolution of color signals, while most conventional approachesresult in blurred edges and false color artifacts. We propose an improvededge-adaptive color interpolation scheme for a progressive scan CCDimage sensor with an RGB color filter array. The proposed edge indicatorfunction utilizes not only the within-channel correlation, but also thecross-channel correlation, and reflects the edge characteristics of an im-age adaptively. The color components unavailable at each channel areinterpolated along the edge direction, not across the edges, so that alias-ing artifacts are suppressed. Furthermore, we eliminate false color arti-facts resulting from the color image formation model in the edge-adaptive color interpolation scheme by adopting the switching algorithmbased on the color edge detection. Simulation results of the proposedalgorithm indicate that the improved edge-adaptive color interpolationscheme produces results that are quantitatively better and visually morepleasing than those achieved by conventional approaches.

This paper presents an aliasing reduction method for digital still cameras (DSCs) which takes advantage of the color- signal correlation. The authors have developed a method of reduc- ing aliasing by increasing the resolution of the color signals in DSCs using a single-chip charge-coupled device with a primary-color filter array. The new method interpolates the signals by taking advantage of the fact that color-signal variations are similar to one another in a local region of an image. In addition, it selects the signal that shows the highest correlation, either in the horizontal or vertical direction, for the color-signal positions subject to interpolation, thus improving color resolution. The effectiveness of the new method has been established: tests show that it significantly reduces aliasing. © 1999 SPIE and IS&T. (S1017-9909(99)01204-0)