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In this paper, we propose a low complexity algorithm to jointly demosaic and denoise Bayer format image, which combines the Hamilton and Adams (HA) method for interpolation and Epsilon filter for noise removal. Instead of using a 5x5 filtering window, one 7x1 horizontal Epsilon filter and one 1x3 vertical Epsilon filter are adopted in our method, which reduces hardware cost significantly while keeps high performance. Simulation results show that our proposed algorithm improves the mean PSNR performance of image by 1 dB compared to the algorithms treating these two processes independently. Furthermore, only 4 line buffers are consumed, and simple logic operators including adders and shifters are used for computation. For real-time implementation, a 5 stage pipelined VLSI architecture with 24 kb SRAM for line buffer is presented. The prototype of the joint processor is verified with Xilinx FPGA device and consumes about 36.6K gates for computational logic after synthesis with TSMC 90nm technology. The joint processor achieves a throughput of 6 Gbps at 250 MHz.

The color cast images usually have serious loss of the color information and are inconvenient for visual observation and image analysis. To tackle this problem, a novel two-stage image color cast correction scheme is proposed in this paper. Firstly, the proposed approach performs the color cast and stable channel detection by using extreme intensity ratio of the original image. Second, the distorted image color is restored by solving the constrained problem with the degree of color variation and the above-detected color cast and stable channel. The experimental results using surveillance videos demonstrate that the proposed scheme is not only feasible but also effectively. In addition, the results satisfy human subjective perception as well.

Image scaling is a very important technique and has been widely used in many image and video processing applications. To achieve the goal of low cost and real time, a novel scaling algorithm is proposed which consists of a bilinear interpolation and an adaptive sharpening filter. The proposed sharpening filter is added to perfect the blurring effects existing in traditional bilinear interpolation methods. Simultaneously, we also verify the scaling quality by taking into account the adaptive technology. Compared with the previous bilinear techniques, our method performs better in terms of both quantitative evaluation and visual quality.

In this paper, we propose a passive scheme for photorealistic computer generated image (PRCG) identification and device brand classification. To this end, a periodic phenomenon of variance of pixel values resulting from demosaicing process is analyzed in RGB color space. Based on the phenomenon, the proposed scheme is composed of color space transformation, high-pass filtering, feature extraction, and classification. To make the phenomenon obvious, a high-pass filter is performed. The periodic phenomenons in different directions are measured as features in the Fourier domain and combined with a hierarchical classifier for PRCG detection and brand identification simultaneously. Experimental results show that our proposed scheme can not only detect photographic images (PIM) but also determine what brand of a camera is used.

In the manufacturing process of image sensors for digital cameras, some defective pixels known as bad pixels will inevitably be produced and more bad pixels may emerge with camera usage over time. These defective pixels can interfere with the demosaicking process, and in this paper, we propose an adaptive method to integrate bad pixel removal with the demosaicking process so that those defective pixels will be excluded in the interpolation process. Depending on the bad pixel location, our proposed adaptive method will adaptively change the order of interpolation so that the bad pixel will be outside the region of interpolation, i.e. if a bad pixel is closer to the point of interpolation, a lower order will be chosen. On the other hand, if the bad pixel is further away, a higher order will be selected for higher accuracy. It has been shown that our proposed adaptive CFA demosaicking method outperforms other existing techniques in terms of interpolation accuracy and bad pixel removal capability.

In image processing, the low resolution problems of imaging system are solved by interpolation techniques. In this paper, we propose an edge-preserving image interpolation method to interpolate the image filmed by linear array camera. For a 1D signal, many signal segments are extracted by a processing window sliding along the signal sequence. For one missing data sample, there are a lot signals segments can be used to recover it. These signal segments are approximated by polynomials, in order to preserve the edges, the one with the minimum fitting error is used to estimate the missing data sample. For a 2D image data, a lot of 1D signals are extracted along directions specified firstly, and then interpolation results are obtained by applying above 1D edge-preserving interpolation method on all these 1D signals. The one with the minimum fitting error is selected as the final output. Experimental treatment results indicate that the proposed method is edge-preserving, and can be used to reconstruct the image filmed by linear array camera effectively.

Image interpolation is a method of obtaining a high resolution image from a low resolution image, which is applied to many image processing procedures. In order to make the interpolated image having smooth edges and make the interpolation processing fast, we propose a fast edge-directed interpolation algorithm in this paper. The proposed method consists of three steps, the determination of nonedge pixels and edge pixels, the bilinear interpolation for nonedge pixels, and the edge-adaptive interpolation for edge pixels. The experimental results show that it outperforms some existing interpolation algorithms in terms of image quality and processing speed.

The wire mesh tomography system directs at the measurement of some points in flow cross-section, so the image reconstruction algorithms are needed for get complete image of flow cross-section. In this paper, nearest neighbor algorithm, average interpolation algorithm, bilinear interpolation algorithm, bi-cubic interpolation algorithm and inverse distance square weighting method are used for image reconstruction, then the spatial resolution can be increased from 16×16 to 64×64.

Image splicing is the most fundamental step of photomontage. In this paper, we propose an efficient blind digital forensics method for image splicing localization. In our method, the demosaicing is used for estimating the natural counterpart of spliced image, which is compared with the test image to expose the abrupt edges along the spliced region. According to the smoothness comparison results, we obtain a binary image to illustrate the localization of the splicing. To evaluate the performance of our method, we apply this approach to DVMM uncompressed spliced image database, and the experimental results indicate the effectiveness on splicing localization.

In this paper, we study the rendering of images with a new mosaic/color filter array (CFA) called the Burtoni mosaic. This mosaic is derived from the retina of the African cichlid fish Astatotilapia burtoni. To evaluate the effect of the Burtoni mosaic on the quality of the rendered images, we use two quality measures in the Fourier domain which are the resolution error and the aliasing error. In our model, no demosaicing algorithm is used, which makes it independent of such algorithms. We also use 11 semantic sets of color images in order to highlight the images classes that are well fitted for the Burtoni mosaic in the process of image acquisition. We have compared the Burtoni mosaic with the Bayer CFA and with an optimal CFA proposed by Hao et al. Experiments have shown that the Burtoni mosaic gives the best performances for images of 9 semantic sets which are the high frequency, aerial, indoor, face, aquatic, bright, dark, step and line classes.

With the advent of low-cost and high-resolution digital cameras and sophisticated editing software, it is becoming increasingly easier to tamper with the digital image. A common form of manipulation is to combine parts of the image fragment into another different image to remove objects from the image. Inspired by the digital image correlation concept, we exploit the peak of cross-correlation function to automatically detect the splicing artifacts in any fragment of an image. We show the efficacy of the proposed scheme on revealing the source of spliced regions. We make the first concrete technique towards appropriate tools which are necessary for rendering digital forgeries.

This paper proposes an effective color-difference-based (ECDB) interpolation algorithm for CFA Image demosaicking. A CFA image consists of a set of spectrally selective filters which are arranged in an interleaved pattern such that only one of color component is sampled at each pixel location. To improve the quality of reconstructed full-color images from color filter array (CFA) images, the ECDB algorithm first analyzes the neighboring samples around a green missing pixel to determine suitable samples for interpolating the value of this green missing pixel. After finishing the interpolation operations of all the green missing pixels, a complete green plane (i.e G plane) can be obtained. The ECDB algorithm then makes use of the high correlation between R, G, and B planes to produce the red- green and blue-green color difference planes and further reconstructs the red and blue planes in successive operations. Because of the green plane provides twice information than red and blue planes, the algorithm exploits the information of green plane more than that of red/blue plane so that the full color image can be reconstructed more accurately. In essence, the ECDB algorithm uses the red-green and blue-green color difference planes, and develops different conditional operations according to the horizontal, vertical, and diagonal neighboring pixel information with suitable weighting technique. The experimental results demonstrate that the proposed algorithm has outstanding performance.

Image resampling method using color edge-directed interpolation has been developed. It uses color image gradient to perform the interpolation across image gradient rather than along image gradient. The developed combined method takes color low resolution image and grayscale high resolution image obtained by a non-linear image resampling method as an input. It includes consecutive calculation stages for high resolution color gradient, for high resolution color information interpolation and finally for high resolution color image assembling. The concept of color basic edges is used to analyze the results of color image resampling. Color basic edge points metric was suggested and used to show the effectiveness of the proposed image interpolation method.

Exposing digital forgeries by detecting local correlation patterns of images has become an important kind of approach among many others to establish the integrity of digital visual content. However, this kind of method is sensitive to JPEG compression, since compression attenuates the characteristics of local correlation pattern introduced by color filter array (CFA) interpolation. Rather than concentrating on the differences between image textures, we calculate the posterior probability map of CFA interpolation with compression related Gaussian model. Thus our approach will automatically adapt to compression. Experimental results on 1000 tampered images show validity and efficiency of the proposed method.

Data hiding is used for raw images instead of the full-resolution colour version. Among various color filter array patterns, we choose the most popular Bayer pattern and cover authentication codes in the pseudo host from original sampling in terms of high correlation within red, blue and green channels. Pseudo host is created by interpolating in this pattern according to the key. This procedure confirms the security, in the meantime, and less brings the artifact of embedding codes. The simulations test the robustness and transparency after some demosaicking methods and JPEG compression. These results imply that hiding data accompany with less demosaicking traces and the better robustness.

The paper introduces the extension of the color image retrieval method based on the approximation of the perceptual parameters. The proposed solution enables effective search for similar images regardlessly of the applied compression scheme not only taking into account the color palette and the presence of regions of the homogenous color within the image, but also their spatial arrangement. The proposed method utilizes the Gaussian Mixture Modeling combined with the Bilateral Filtering approach along with color matching method based on dominant region color. The evaluated results show that satisfactory retrieval results can be obtained regardlessly to applied compression schemes, preserving the spatial arrangement of the color regions in evaluated results.

The main problem addressed in this paper is as follows: a system applying the proposed framework should retrieve all images whose color structure is similar to that of the given query image, independently on the applied lossy coding. We propose an approach based on the color histogram approximation using the Gaussian Mixture Model. The proposed method incorporates the information on the spatial distribution of the color image pixels utilizing the bilateral filtering scheme. The retrieval results were evaluated on large databases of natural color images and the usefulness of the proposed technique was compared with some commonly known retrieval methods operating on color histograms.

A new approach to the restoration of color images corrupted by Gaussian noise is presented. The proposed technique adopts a multipass processing approach that gradually reduces the noise in the color information components of the image. Two different models for data smoothing are proposed based on the different classes of noisy pixels. The subsequent algorithm for edge detection is designed to better appraise the noise cancellation behavior of our filter from the point of view of human perception. This method does not require any “a priori” knowledge about the amount of noise corruption. Experimental results show that the filtering performance of the proposed approach is very satisfactory and accurate edge maps are achieved even in the presence of highly corrupted data.

In this paper, a new wavelet-based image interpolation algorithm is developed for magnifying the image details so that the visibility of tiny features in a digital image is improved. The algorithm takes the LR image as the low-pass filtered subband of an unknown wavelet transformed high resolution image. Then an initial HR image of size twice the LR image is estimated using zero padding of the details. The HR image is transformed via UWT resulting in four subbands, three of which are related with the high frequency components of the image. In the UWT domain, the LL subband is replaced with the initially estimated HR image and applying the inverse UWT, the final HR image is determined. Experiments conducted with both gray level and color images show the superiority of the proposed algorithm over the state-of-the-art interpolation methods.

In this paper, a novel fragile watermarking method is proposed for color image recovery via color filter array interpolation. First we turn a color image into a grey image by color sampling in the form of Bayer patterns. Data of the grey image which is reordered as reference bits are then embedded into the original image along with some authentication bits generated from the image. After the watermarked image is received, authentication bits are extracted to identify which regions are tampered. When the image is detected to be tampered, extracted reference bits are used to reconstruct a color image by color filter array interpolation, which is then used to recover the content of the tampered regions. Experiments show this method has good recovery ability.

In this paper, we introduce an edge detection algorithm for mosaiced images which can be used to enhance generic demosaicing algorithms. The algorithm is based on pixels color differences in the horizontal, vertical and diagonal directions. By using our edge-detection technique to enhance the universal demosaicing algorithm of Lukac et al., experimental results show that the presence of color shifts and artefacts in demosaiced images is reduced. This is confirmed in regard to both subjective and objective evaluation.

Most digital cameras use a color filter array of mosaic pattern to capture the colors of the scene. To render a full-resolution color image using a singlechip camera, the missing information must be estimated from the surrounding pixels. In this paper, we present an edge-adaptive demosaicking method for color demosaicking. The algorithm first estimate missing green samples in red and blue position by determining direction of edge in horizontal, vertical and diagonal directions. After determining the green samples, missing red and blue samples estimated using color differences along the direction of edges. Experimental results demonstrate that the proposed method produces visually pleasing images and significantly outperforms existing demosaicking methods in terms peak signal-tonoise ratio (PSNR) and ΔEab, which is a measure for the average color distance between original and demosaicked images in the CIELAB color space.

A gradient-based adaptive interpolation method is proposed in this paper to solve the over-blurring problem in conventional multiple view synthesis (MVS) filters. To improve the visual quality of final synthetic pictures, a good interpolation filter is required in multiple view synthesis steps. Traditional space-invariant filters, such as bi-linear or bi-cubic filter, take the advantage of complexity, but they also lead to a decrease of the subjective quality. In contrast, directional filters usually exploit the directional information, especially in edge area, to deal with the over-blurring problem. This paper proposes a fast directional interpolation method for scaling up the resolution or filling up the losing pixels of a picture. The gradient map of an input picture is calculated in first. Using the gradient of each input pixel, the interpolation coefficients computed from a Gaussian kernel is refined, leading to a directional filter which takes an adaptation with gradient direction. This method is with a low complexity because it is a non-iterative method, and experiment results show that the visual quality of interpolated picture is improved.

In an era where technology keeps advancing, it is vital that high-resolution images are available to produce high-quality displayed images and fine-quality prints. The problem is that it is quite impossible to produce high-resolution images with acceptable clarity even with the latest digital cameras. Therefore, there is a need to enlarge the original images using an effective and efficient algorithm. The main contribution of this paper is to produce an enlarge color image with high visual quality, up to four times the original size of 100x100 pixels image. In the classification phase , the basic idea is to separate the interpolation region in the form of geometrical shape. Then, in the intensity determination phase , the interpolator assigns a proper color intensity value to the undefined pixel inside the interpolation region. This paper will discuss about problem statement, literature review, research methodology, research outcome, initial results, and finally, the conclusion.

In this paper we investigate `real-time' watermarking of single-sensor digital camera images (often called `raw' images) and blind watermark detection in demosaicked images. We describe the software-only implementation of simple additive spread-spectrum embedding in the firmware of a digital camera. For blind watermark detection, we develop a scheme which adaptively combines the polyphase components of the demosaicked image, taking advantage of the interpolated image structure. Experimental results show the benefits of the novel detection approach for several demosaicking techniques.

A two phased impulse restoration scheme for color images is presented. In the first phase of the proposed image restoration scheme it detects the pixels corrupted with impulse noise by employing the tools of mathematical morphology and then in the next phase a linear adaptive mean filter attempts to remove those noisy pixels in an efficient manner. Experimental results indicate that the proposed scheme can suppress impulse noise effectively in color images. This provides a better restoration performance than many other filters used for removing impulse noise from color images.

This paper proposes a fast and effective image interpolation algorithm using median filter. The interpolation algorithm is composed of two steps. First, a non-linear iterative procedure is utilized to interpolate the pixel whose direction can be easily determined by local information. Second, according to the introduced assumption that image interpolation can be regarded as a local image filtering process, the remaining pixels are interpolated by the proposed fast median filter method. Experimental results show that the proposed algorithm provides better performance than traditional techniques (e.g. bilinear interpolation, bicubic interpolation) both in subjective quality and objective quality with similar complexity. In particular, the proposed method is comparable to the well-known NEDI algorithm in visual quality, however, with much lower computational complexity. Therefore, the proposed algorithm can be exploited for real-time applications due to the merits of low computational complexity and good image quality.

Source camera identification is an important branch of image forensics. This paper describes a novel method for determining image origin based on color filter array (CFA) interpolation coefficient estimation. To reduce the perturbations introduced by a double JPEG compression, a covariance matrix is used to estimate the CFA interpolation coefficients. The classifier incorporates a combination of one-class and multi-class support vector machines to identify camera models as well as outliers that are not in the training set. Classification experiments demonstrate that the method is both accurate and robust for double-compressed JPEG images.

This paper presents a spatial noise reduction technique designed to work on CFA (Color Filter Array) data acquired by CCD/CMOS image sensors. The overall processing preserves image details by using heuristics related to HVS (Human Visual System) and texture detection. The estimated amount of texture and HVS sensitivity are combined to regulate the filter strength. Experimental results confirm the effectiveness of the proposed technique.

A new realtime interpolation algorithm for color image is presented. The algorithm is based on the concept of indiscernibility relation in rough sets (RS) theory. By applying the concept of upper and lower approximation based on the continuity of images, the image is first divided into homogenous area, edge pixels and isolated pixels. Then Bezier surface interpolation is further achieved using the information of classification. Besides emulation, the technology has been applied to the visual presenter with low-resolution image sensor. Results demonstrate that the new algorithm improves substantially the subjective and objective quality of the interpolated images over conventional interpolation algorithms, and meets the requirements of real time image processing. The algorithm represents an attempt to incorporate RS in image processing.

A new method for image processing, especially for image zooming by bivariate rational interpolation based on function values is presented. Suppose that the given data points of an image are sampled from an original surface, then the continuous interpolation surface is constructed based on rational spline to fit the original surface. The image resized can be obtained just by resampling on the interpolation surface. The new method produces pictures with higher precision, which is useful in 2D CT image resizing. According to the special property of CT image, the value of new resampling data point can be taken from a region. The experimentations show that the proposed method can maintain clear border of zoomed image and hence offer more detail information in real application for doctors. The algorithm is simple and efficient in computation.

The Vector Median M-type L (VMML) -filter to remove impulsive noise from color images and video color sequences is presented. This filter utilizes multichannel image processing by using the vector approach and the Median M-Type L (MML) algorithm. Simulation results indicate that the proposed filter consistently outperforms other color image filters by balancing the tradeoff between noise suppression, detail preservation, and color retention.

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).

The article presents two practical ways of using the automated color image segmentation in the medical field: for content-based region query and for tracking the time evolution of the disease in patients following a certain treatment. A known technique was used for automated color medical image segmentation - the color set back-projection algorithm. Our previous work in extraction of color regions from a database of nature images using the same algorithm showed promising results. The images are transformed from RGB to HSV color space, quantized at 166 colors and processed by the color set backprojection algorithm that allows the color region detection. The algorithm is studied from two points of view: complexity and the retrieval quality. The experiments that were made on a database with color endoscopy images from digestive tract have shown satisfying results for both applications that are important in practical medical use and medical teaching.

Fiber optics are widely used in flexible endoscopes which are indispensable for many applications in diagnosis and therapy. Computer-aided use of fiberscopes requires a digital sensor mounted at the proximal end. Most commercially available cameras for endoscopy provide the images by means of a regular grid of color filters what is known as the Bayer Pattern. Hence, the images suffer from false colored spatial moire, which is further stressed by the downgrading fiber optic transmission yielding a honey comb pattern. To solve this problem we propose a new approach that extends the interpolation between known intensities of registered fibers to multi channel color applications. The inventive idea takes into account both the Gaussian intensity distribution of each fiber and the physical color distribution of the Bayer pattern. Individual color factors for interpolation of each fiber area make it possible to simultaneously remove both the comb structure from the fiber bundle as well as the Bayer pattern mosaicking from the sensor while preserving depicted structures and textures in the scene.

In this paper we present a stable automatic system for image composition, which can well control the color difference between two images, and produce a seamless composite image with color continuity. This is a user-friendly system that reduces the user's manual tasks. We observe that Poisson image editing written by Perez et al. [8] blends well for seamless boundary automatically. However, the color of user-selected region can be changed after applying this method. So the object loses its original color tone after blending. To solve this problem, firstly we check out the case of object color being changed rapidly. It can be done by calculating color temperatures of two input images and comparing the white balance with each other. Next, a distance ratio rule is applied to controls the pixels included in the region between the user-selected boundary and object boundary.

PDE (Partial differential equation) is an image interpolation method which interpolates based on local geometry property. It can not preserve texture pattern and can only process natural image. NL (Non Local)-means is an interpolation method that uses global information of image. Entire texture pattern in image can be well preserved because of the high replication property of NL-means, while the problem is that blur is preserved as well. In this paper a novel image interpolation method which combines PDE and NL-means is proposed. Image interpolated by the novel method is clear and smooth, and preserves texture pattern. The new method enhances edges using shock filter PDE which does not strengthen jaggies of block contour in interpolated image; the PDE used in this method to smooth image diffuses along level curve. Divided gray regions caused by PDE are smoothed by NL-means; the broken texture pattern is recovered well. Lastly, it is proved that even noisy image can be directly interpolated to the required size using this method. Both theoretical analysis and experiments have been used to verify the benefits of the novel interpolation method.

We propose a nonlinear image interpolation method, based on an anisotropic diffusion PDE and designed for the general case of vector-valued images. The interpolation solution is restricted to the subspace of functions that can recover the discrete input image, after an appropriate smoothing and sampling. The proposed nonlinear diffusion flow lies on this subspace and its strength and anisotropy effectively adapt to the local variations and geometry of image structures. The derived model efficiently reconstructs the real image structures, leading to a natural interpolation, with reduced blurring, staircase and ringing artifacts of classic methods. This method also outperforms other existing PDE-based interpolation methods. We present experimental results that prove the potential and efficacy of the method as applied to graylevel and color images.

A generalized regression neural network based error correction scheme for linear image interpolation approach is proposed. A middle image with the same size of source image is obtained by interpolating a down-sampled image from the source image. Then neural network is established with employing the interpolation error between the source image and the middle image. Finally interpolation correction is applied to the linear interpolation result of source image using neural network estimation to obtain more accuracy result image. Experimental results of the proposed approach demonstrate the effectiveness of the scheme.

The rendering of lower resolution image data on higher resolution displays has become a very common task, in particular because of the increasing popularity of webcams, camera phones, and low-bandwidth video streaming. Thus, there is a strong demand for real-time, high-quality image magnification. In this work, we suggest to exploit the high performance of programmable graphics processing units (GPUs) for an adaptive image magnification method. To this end, we propose a GPU-friendly algorithm for image up-sampling by edge-directed image interpolation, which avoids ringing artifacts, excessive blurring, and staircasing of oblique edges. At the same time it features gray-scale invariance, is applicable to color images, and allows for real-time processing of full-screen images on today's GPUs.

Color-based particle filter for object tracking has been an active research topic in recent years. Despite great efforts of many researchers, there still remains to be solved the problem of contradiction between efficiency and robustness. The paper makes an attempt to partially solve this problem. Firstly, the Integral Histogram Image is introduced by which histogram of any rectangle region can be computed at negligible cost. However, straightforward application of the Integral Histogram Images causes the problem of “curse of dimensionality”. In addition, traditional histogram is inefficient and inaccurate. Thus we propose to adaptively determine histogram bins based on K-Means clustering, which can represent color distribution of object more compactly and accurately with as a small number of bins. Thanks to the Integral Histogram Images and the clustering based color histogram, we finally achieve a fast and robust particle filter algorithm for object tracking. Experiments show that the performance of the algorithm is encouraging.

Tongue image matching is an important part for a tongue diagnosis system. Unlike common pattern recognition problems, it is hard to define the ground truth for tongue image matching because visual inspection of tongue by doctors is determined by the experience and knowledge of them. Here we propose to use, Mean Rank, as an objective and scientific criterion to evaluate matching performance. Instigating from color demosaicking, a new color texture operator, Primary Difference Signal Local Binary Pattern is proposed. The matching performance is evaluated on color, gray-scale and color texture, and fusion of color and texture features.

We propose an image file compression method using Region Growing and Interpolation. Firstly, using the techniques of Region Growing, we divide the image in blocks. The compression-decompression method is based on bivariate interpolation. Error estimates in terms of the modulus of continuity of the original image are obtained. Experimental results illustrate the performances of the proposed method.

In this paper, we propose fast color interpolation methods for color filter array (CFA). The proposed methods exploit correlations among the three color components. It has been reported that the quality of color interpolation can be significantly improved by utilizing correlation among color components. However, the complexity of such methods may increase significantly compared to those methods which separately interpolate each color component. On the other hand, the major application of CFA is portable imaging devices such as digital cameras or mobile phones. For portable devices, power consumption is an important issue. In this paper, we propose high quality color interpolation methods with reduced complexity. Experimental results show that the proposed methods provide noticeable improvements without significantly increasing the system complexity.