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 ...
Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational complexity in dictionary learning. Second, each patch is considered independently in dictionary learning and sparse co...
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) ...
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",...
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......
摘要: s This paper applies group based sparse representation to solve the restoration problem of Kronecker compressive sensing for still images. Our simulation results validate that the proposed method outperforms the state-of-the art reconstructions and existing sensing systems....
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 Restoration Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimizatio... J Zhang,D Zhao,G Wen - 《IEEE Transactions on Image Processing A Publication of ...
In this paper, we propose a group sparse representation model using multiple features for object tracking. By appropriately selecting features to form the dictionary, the group sparse based representation scheme can assign proper weights to each group of selected templates. As a result, the potential...
Based on the block-restricted isometry property (BRIP), we establish some sufficient conditions for recovering the support of the block ??-sparse signals via block orthogonal matching pursuit (BOMP) algorithm. Under some constraints on the minimum magnitude of the no...