Video Denoising, Deblocking and Enhancement Through Separable
1 Video Denoising, Deblocking and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms Matteo Maggioni, Giacomo Boracchi, Alessandro Foi, Karen Egiazarian Abstract—We propose a powerful video filtering algorithm that the nonlocal similarity between patches at different locations exploits temporal and spatial redundancy characterizing natural within the data [5], [6]. Algorithms based on this approach video sequences. The algorithm implements the paradigm of have been proposed for various signal-processing problems, nonlocal grouping and collaborative filtering, where a higher- dimensional transform-domain representation of the observations and mainly for image denoising [4], [6], [7], [8], [9], [10], [11], is leveraged to enforce sparsity and thus regularize the data: [12], [13], [14], [15]. Specifically, in [7] has been introduced 3-D spatiotemporal volumes are constructed by tracking blocks an adaptive pointwise image filtering strategy, called non- along trajectories defined by the motion vectors. Mutually similar local means, where the estimate of each pixel xi is obtained volumes are then grouped together by stacking them along an as a weighted average of, in principle, all the pixels x of additional fourth dimension, thus producing a 4-D structure, j termed group, where different types of data correlation exist the noisy image, using a family of weights proportional to along the different dimensions: local correlation along the two the similarity between two neighborhoods centered at xi and dimensions of the blocks, temporal correlation along the motion xj. So far, the most effective image-denoising algorithm is trajectories, and nonlocal spatial correlation (i.e. self-similarity) BM3D [10], [6], which relies on the so-called grouping and along the fourth dimension of the group.
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