In this paper, a novel projection onto convex sets (POCS) method is presented for the suppression of blocking and ringing artifacts in a compressed image that contains homogeneous regions. A new family of convex smoothness constraint sets is introduced, using the uniformity property of image regions. This set of constraints allows different degrees of smoothing in different regions of the image, while preserving the image edges. The regions are segmented using the fuzzy c-means algorithm, which allows ambiguous pixels to be left unclassified. Experimental results on JPEG compressed images demonstrate that the proposed algorithm yields visually superior images compared to several of the recently reported POCS deblocking algorithms for the class of images considered.Index Terms-Blocking artifacts, fuzzy C-means, image deblocking, projection onto convex sets, region homogeneity constraints. I. INTRODUCTIONT HE BLOCK discrete cosine transform (BDCT) is a commonly used transform for both still and moving image coding, such as in JPEG and MPEG. It is based on dividing an image into small blocks, normally 8 8, computing the DCT of each block, and quantizing the DCT coefficients according to a predefined quantization table. The DCT has become a prominent technique for image compression since it is an asymptotic approximation to the optimal Karhunen-Loeve Transform (KLT) when the statistical properties of the image can be represented by a first-order Markov model. However, a major drawback of BDCT-based techniques is that the compressed image exhibit visually annoying blocking and ringing artifacts, especially at high compression ratio. Blocking artifacts appear due to quantizing low-frequency coefficients, which gives rise to discontinuity between intensities of two adjacent blocks, whereas ringing artifacts are caused by discarding high-frequency coefficients.Projection onto convex sets (POCS) is a method for solving many constraint satisfaction and optimization problems and has Manuscript
An improved algorithm for planar rotational motion artifact suppression in standard two-dimensional Fourier transform magnetic resonance images is presented. It is shown that interpolation of acquired view data on the uncorrupted k-space create data overlap and void regions. We present a method of managing overlap data regions, using weighted averaging of redundant data. The weights are assigned according to a priority ranking based on the minimum distance between the data set and the k-space grid points. An iterative estimation technique for filling the data void regions, using projections onto convex sets (POCS), is also described. The method has been successfully tested using computer simulations.
This paper presents a color processing architecture for digital color cameras utilizing complementary metal oxide semiconductor (CMOS) image sensors. The proposed architecture gives due consideration to the peculiar aspects of CMOS image sensors and the human visual perception related to the particular application of digital color photography. A main difference between the proposed method arid the conventional systems is the fact that color correction module is located before the interpolation module. Therefore, a method of performing color correction on a color filter array (CFA) pattern is also provided in this paper. The interpolation algorithm is especially designed to solve the problem of pixel cross talk among the pixels of different color channels. The algorithm separates the green channel into two planes, one highly correlated with the red channel and the other with the blue channel. These separate planes are used for red and blue channel interpolation. The implementation details related to managing four color channel values is also described. Experiments conducted on McBeth color chart and natural images have shown that the proposed color processing chain produces better quality images with improved SNR. Disciplines Physical Sciences and Mathematics Publication DetailsLi, W., Kharitonenko, I., Weerasinghe, C. Novel Color Processing Architecture for Digital Cameras with CMOS Image SensorsChaminda Weerasinghe, Wanqing Li, Igor Kharitonenko, Magnus Nilsson and Sue TwelvesAbstract -This paper presents a color processing architecture for digital color cameras utilizing Complementary Metal Oxide Semiconductor (CMOS) image sensors. The proposed architecture gives due consideration to the peculiar aspects of CMOS image sensors and the human visual perception related to the particular application of digital color photography. A main difference between the proposed method and the conventional systems is the fact that color correction module is located before the interpolation module. Therefore, a method of performing color correction on a color filter array (CFA) pattern is also provided in this paper. The interpolation algorithm is especially designed to solve the problem of pixel cross talk among the pixels of different color channels. The algorithm separates the green channel into two planes, one highly correlated with the red channel and the other with the blue channel. These separate planes are used for red and blue channel interpolation. The implementation details related to managing four color channel values is also described. Experiments conducted on McBeth color chart and natural images have shown that the proposed color processing chain produces better quality images with improved SNR.
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