A regularization approach to demosaicking
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Abstract:
Demosaicking is the process of reconstructing a full resolution color image from the sampled data acquired by a digital camera that apply a color filter array to a single sensor. In this paper, we propose a regularization approach to demosaicking, making use of some prior knowledge about natural color images, such as smoothness of each single color component and correlation between the different color channels. Initially, a quadratic strategy is considered and a general approach is reported. Then, an adaptive technique is analyzed, in order to improve the reconstruction near the edges and the discontinuities of the image. This is performed using a novel strategy that avoids computational demanding iterations. The proposed approach provides good performances and candidates itself for many applications.Keywords:
Demosaicing
Regularization
Color filter array
Demosaicking is a process of obtaining a full color image by interpolating the missing colors of an image captured from a digital still and video cameras that use a single-sensor array. In this study a new Color Filter Array (CFA) is proposed. Which is based on the actual weight of the Human Visual System. It is developed based on the sensitivity level of the human eye to red as 29.9%, green as 58.7% and blue as 11.4%. This study also provides an effective iterative demosaicing algorithm applying a weighted-edge interpolation to handle green pixels followed by a series of color difference interpolation to update red, blue and green pixels. Before applying demosaicking algorithm Gaussian filter is applied to remove noise of the sensor applied image and also enhance the image quality. Experimental results show that the proposed method performs much better than other latest demosaicing techniques in terms of image quality and PSNR value.
Demosaicing
Color filter array
Bayer filter
Interpolation
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In a single-chip digital color imaging sensor, a color filter array (CFA) is used to obtain sampled spectral components (red, green and blue) in an interleaved fashion. Color demosaicing is the process of interpolating these regularly spaced sampled values into a dense pixel map for each spectral component. We present two new color interpolation techniques with low buffer requirements for processing while developing color images with very good quality. We also present a comparative study with other interpolation techniques of similar buffer requirements.
Demosaicing
Color filter array
Bayer filter
Interpolation
False color
Color balance
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Citations (6)
According to the physical structure of color image sensor in camera, cameras acquire images using image sensors overlaid with a color filter array (CFA) from different channels filters, so we can only achieve a single color component at each pixel position. In order to reconstruct a color image, color demosaicing is required to reconstruct the other two color components. General interpolation method may blur the image edge and introduce visible artifacts near edges. An image reconstruction algorithm based on adaptive region demosaicing is put forward in Bayer format to reduce the color artifacts. Experiment results show that the algorithm can improve the image quality and PSNR, sharpen texture and edge of the image and enhance image quality.
Demosaicing
Color filter array
Bayer filter
Image gradient
Color histogram
False color
Color balance
Interpolation
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Demosaicing
Color filter array
Chrominance
Color depth
Color histogram
Color balance
JPEG
Interpolation
False color
RGB color model
Color quantization
Bayer filter
High color
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Citations (21)
Color filter array (CFA) is one of the key points for single-sensor digital cameras to produce color images. Bayer CFA is the most commonly used pattern. In this array structure, the sampling frequency of green is two times of red or blue, which is consistent with the sensitivity of human eyes to colors. However, each sensor pixel only samples one of three primary color values. To render a full-color image, an interpolation process, commonly referred to CFA demosaicing, is required to estimate the other two missing color values at each pixel. In this paper, we explore an adaptive progressive interpolation based on the edge type algorithm. The proposed demosaicing method consists of two successive steps: an interpolation step that estimates missing color values according to various edges and a post-processing step by iterative interpolation.
Demosaicing
Color filter array
Bayer filter
Interpolation
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Color Filter Arrays (CFA) used by single sensor cameras captures single color information at each pixel location. The process of estimating the missing color samples to reconstruct a full color image is called color filter array interpolation or demosaicing. Demosaicing the CFA images without denoising leads to demosaicing artifacts that will reduce the perceptual quality of the image. This paper presents a denoising before demosaicing strategy for denoising of CFA images captured by single sensor cameras. The images captured by the camera using the Bayer pattern are first denoised by Principal Component Analysis and followed by multiscale gradient based demosaicing to preserve the edges and details. The demosaicing strategy is followed by a false color suppression method to remove the residual demosaicing artifacts.
Demosaicing
Color filter array
Bayer filter
Interpolation
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Citations (2)
Conventional single-chip digital cameras use color filter arrays(CFA) to sample different spectral components. Image demosaicing is a problem of interpolating these data to complete red, green, and blue values for each image pixel, to produce an RGB image. Many color demosaicing(CDM) methods assume that the high local spatial redundancy exists among the color samples. Such an assumption, however, may be fail for images with high color saturation and sharp color transitions. This paper presents an adaptive demosaicing algorithm by exploiting both the non-local similarity and the local correlation(NLS-LC) in the color filter array image. First, the most flattest nonlocal image patches are searched in the searching window centered on the estimated pixel. Second, the patch, which is the most similar to the current patch, is selected among the most smoothest nonlocal patches. Third, according to the similar degree and the local correlation degree, the obtained nonlocal image patch and the current patch are adaptively chosen to estimate the missing color samples. Experimental results indicate that the proposed method exhibits superior performance over many state-of-the-art color interpolation methods.
Demosaicing
Bayer filter
Color filter array
RGB color model
Color balance
Color depth
Color histogram
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Citations (5)
A color interpolation scheme for a progressive scan CCD image sensor with a RGB color filter array is required to overcome the physical limitation of the CCD image sensor and to increase the resolution of color signals. Most conventional approaches result in blurred edges and false color artifacts. In this paper, we propose an improved edge-adaptive color interpolation scheme for a progressive scan CCD image sensor. The proposed edge indicator function uses not only the within-channel correlation but also the cross-channel correlation, and reflects the edge characteristics in an image adaptively. The color components unavailable at each channel are interpolated along the edge direction, not across the edges, so that aliasing artifacts are suppressed. Furthermore, we eliminated false color artifacts resulting from the color image formation model in the edge-adaptive color interpolation scheme by adopting the switching algorithm that is based on the color edge detection. Simulation results of the proposed algorithm indicate that the improved edge-adaptive color interpolation scheme produces better quantitative and visually pleasing results than other conventional approaches.
Demosaicing
Color filter array
RGB color model
Interpolation
Image gradient
Color histogram
False color
Color balance
Color depth
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Citations (31)
Image demosaicking or color filter array interpolation is a process of interpolating missing color samples to reconstruct a full color image. In general, existing algorithms assume that the high frequency components such as edges, texture etc. of different color channels are similar and thus take an advantage of it to estimate the missing samples. In this paper, we efficiently analyze the relationships of intra and inter-color correlation among the channels and observe that such assumption fails in most cases. In view of this observation, we propose a scheme that exploits the correlation between different color channels much effectively than the existing algorithms. Experimental results demonstrate that the proposed algorithm outperforms the existing methods both in terms of Peak Signal to Noise Ratio (PSNR) and visual perception.
Demosaicing
Color filter array
Interpolation
Peak signal-to-noise ratio
Color histogram
Colors of noise
Color balance
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Citations (30)
Many image capture devices use a single image sensor covered with a color filter array to capture color images. The color filter array allows only one color to be measure at each pixel. This means the device must estimate the missing two color values at each pixel. This paper introduce a few commonly used color interpolate algorithms based on Bayer color filter array. These color interpolate algorithms are farther compared in terms of the image quality and the computational complexity. Experimental results show that bilinear interpolation algorithm has the lowest computational complexity and adaptive color plane interpolation has the best image quality.
Demosaicing
Color filter array
Bayer filter
Color histogram
Interpolation
Color depth
Color balance
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Citations (1)