Structural Group Sparse Representation for Image Compressive Sensing Recovery
This work addresses image compressive sensing recovery for applications like medical imaging or surveillance, offering a novel method that is incremental in improving sparsity and self-similarity modeling.
The paper tackles the problem of poor rate-distortion performance in image compressive sensing recovery by proposing a structural group sparse representation framework that enforces sparsity and self-similarity, achieving significant performance improvements over state-of-the-art methods with demonstrated convergence.
Compressive Sensing (CS) theory shows that a signal can be decoded from many fewer measurements than suggested by the Nyquist sampling theory, when the signal is sparse in some domain. Most of conventional CS recovery approaches, however, exploited a set of fixed bases (e.g. DCT, wavelet, contourlet and gradient domain) for the entirety of a signal, which are irrespective of the nonstationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor rate-distortion performance. In this paper, we propose a new framework for image compressive sensing recovery via structural group sparse representation (SGSR) modeling, which enforces image sparsity and self-similarity simultaneously under a unified framework in an adaptive group domain, thus greatly confining the CS solution space. In addition, an efficient iterative shrinkage/thresholding algorithm based technique is developed to solve the above optimization problem. Experimental results demonstrate that the novel CS recovery strategy achieves significant performance improvements over the current state-of-the-art schemes and exhibits nice convergence.