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In this paper, a group sparse model using Eigenvectors of the Graph Laplacian (EGL) is proposed for image denoising. Unlike the heuristic setting for each image and for each noise deviation in the traditional denoising method via the EGL, in our group-sparse-based method, the used eigenvectors are adaptively selected with the error control. Sequentially, a modified group orthogonal matching pursuit...
In this paper, a sparse approximation algorithm using eigenvectors of the graph Laplacian is proposed for image denoising, in which the eigenvectors of the graph Laplacian of images are incorporated in the sparse model as basis functions. Here, an eigenvector-based sparse approximation problem is presented under a set of residual error constraints. The corresponding relaxed iterative solution is also...
Sparse representation theory has been well developed in recent years. In this paper, we consider an image denoising problem which can be efficiently solved under the framework of the sparse representation theory. The traditional image denoising methods based on the sparse representation seldom take into account the special structure of the data. As an attempt to overcome such problem, the Graph regularized...
Rate-distortion (R-D) model that includes rate-quantization (R-Q) and distortion-quantization (D-Q) models is very important in rate control and mode decision for video coding algorithm optimization. R-D model can be derived by rigorous mathematic analysis based on DCT coefficient distribution. Thus, the statistical distribution of DCT coefficients is very important for R-D modeling. Laplacian, Cauchy,...
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