Recently, the sparse representation (SR) was used to solve BS problem, called SR-BS. It aimed to find a set of representative bands that can represent the whole bands based on the minimization of reconstructed error in SR. However, those SR-BS methods suffer from an issue about higher ...
Then, the new degradation model is integrated into the group-based sparse representation framework. Finally, the single image dehazing problem is regarded as an image restoration problem, which can be well optimized by GSR. The method ... X Wang,X Zhang,H Zhu,... 被引量: 0发表: 2020年 ...
Group-based sparse representationNonlocal total variationJoint regularizationSplit Bregman iterationCompressive sensing (CS) has recently drawn considerable attentions in signal and image processing communities as a joint sampling and compression approach. Generally, the image CS reconstruction can be formulated...
In this paper, in order to reduce blocking artifacts and obtain high-quality image, image deblocking is formulated as an optimization problem via maximum a posteriori framework, and a novel algorithm for image deblocking using group-based sparse representation (GSR) and quantization constraint (QC) ...
Unsupervised Band Selection Based on Group-Based Sparse RepresentationBand selection (BS) is one of the important topics in hyperspectral image data analysis. How to search the representative bands that can effectively represent the image with lower inter-band......
Group-based Sparse Representation for Image Restoration (Matlab Code) These MATLAB programs implement the image restoration algorithms via group-based sparse representation (GSR) modeling as described in paper: Jian Zhang, Debin Zhao, Wen Gao, "Group-based Sparse Representation for Image Restoration",...
Recent advances have suggested that group sparse representation based models, which exploit nonlocal self-similarity prior, lead to superior results in image CS recovery. In this paper, we propose a CS recovery method via group sparse representation with nonconvex LLp norm regularization (GSR-LLp)....
Group-based sparse representation for image compressive sensing reconstruction with non-convex regularization - ScienceDirect Patch-based sparse representation modeling has shown great potential in image compressive sensing (CS) reconstruction. However, this model usually suffers ... Zhiyuan Zha a,Xinggan ...
Group-based sparse representation for image compressive sensing reconstruction with non-convex regularization. Neurocomputing 2018, 296, 55–63. [Google Scholar] [CrossRef] [Green Version] Ren, X.; Bai, Y.; Jiang, Y. Hybrid spatial model for fast terahertz imaging. Micromachines 2021, 12, ...
-sparse signals via block orthogonal matching pursuit (BOMP) algorithm. Under some constraints on the minimum magnitude of the nonzero elements of the block ??-sparse signals, we prove that the support of the block ??-sparse signals can be exactly recovered by ...