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We propose a novel noisy low-light image enhancement algorithm via structure-texture-noise (STN) decomposition. We split an input image into structure, texture, and noise components, and enhance the structure and texture components separately. Specifically, we first enhance the contrast of the structure image, by extending a 2D histogram-based image enhancement scheme based on the characteristics...
An efficient stereo matching algorithm, which applies adaptive smoothness constraints using texture and edge information, is proposed in this work. First, we determine non-textured regions, on which an input image yields flat pixel values. In the non-textured regions, we penalize depth discontinuity and complement the primary CNN-based matching cost with a color-based cost. Second, by combining two...
A novel probabilistic depth-guided multi-view denoising (PDMD) algorithm is proposed in this work. We formulate the multi-view image denoising problem by considering the uncertainties in depth estimates in noisy environments. Specifically, we employ the geometric distributions of nonlocal neighbors, as well as the block similarities, to approximate the probabilities of depth estimates. We then use...
A nonlocal minimum mean square error (MMSE) image denoising algorithm to remove Poisson noise is proposed in this work. Based on the Bayesian estimation theory, we first derive the nonlocal MMSE denoising filter, which can minimize the mean square error (MSE) of a denoised block. Then, we develop an approximation of the filter for practical implementation. Simulation results show that the proposed...
A novel concept of channel division to improve the performance of distributed video coding is proposed in this work. At the decoder, the proposed algorithm partitions each side information frame into multiple regions with different expected distortions. It is shown that the partitioning is equivalent to the division of the virtual noisy channel from the encoder to the decoder into multiple channels...
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