Title :
Structural Group Sparse Representation for Image Compressive Sensing Recovery
Author :
Jian Zhang ; Debin Zhao ; Feng Jiang ; Wen Gao
Author_Institution :
Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China
Abstract :
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, contour let 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.
Keywords :
Nyquist criterion; compressed sensing; convergence; fractals; image coding; image representation; image sampling; iterative methods; optimisation; Nyquist sampling theory; SGSR; adaptive group domain; convergence; image compressive sensing recovery; image self-similarity; image sparsity; iterative shrinkage algorithm; iterative thresholding algorithm; natural signal nonstationarity; optimization; signal decoding; sparse signal; structural group sparse representation; Adaptation models; Compressed sensing; Dictionaries; Educational institutions; Image coding; Transforms; Vectors; compressive sensing; image recovery; sparsity; structural group sparse representation;
Conference_Titel :
Data Compression Conference (DCC), 2013
Conference_Location :
Snowbird, UT
Print_ISBN :
978-1-4673-6037-1
DOI :
10.1109/DCC.2013.41