Adaptive gradient-based block compressive sensing with sparsity for noisy images

Abstract

This paper develops a novel adaptive gradient-based block compressive sensing (AGbBCS SP) methodology for noisy image compression and reconstruction. The AGbBCS SP approach splits an image into blocks by maximizing their sparsity, and reconstructs images by solving a convex optimization problem. In block compressive sensing, commonly used square block shapes cannot always produce the best results. The main contribution of our paper is to provide an adaptive method for block shape selection, improving noisy image reconstruction performance. The proposed algorithm can adaptively achieve better results by using the sparsity of pixels to adaptively select block shape. Experimental results with different image sets demonstrate that our AGbBCS SP method is able to achieve better performance, in terms of peak signal to noise ratio (PSNR) and computational cost, than several classical algorithms.

Sparse and Incomplete Signal Dictionaries for Reconstruction of MR Images

“…Let y=Af + e be the observed vector of samples with arbitrary signals of K-sparse amalgamated with e noise. With precision parameter έ, CoSaMP produces k-sparse estimation that is fulfilled by the equation (14).…”

Performance Enhancement of Bit-Level XOR Compressed Image OFDM Transmission Systems

“…In [10], the authors studied the compressed image OFDM transmission based on the well-known EZW or SPIHT algorithms. en to reduce the computational complexity of CS, the block compressed sensing (BCS) method was proposed [13][14][15][16][17]. In [15], the transmission performance of the block compressed-sensed image data over a wireless channel was researched, and the simulation results show that BCS with a proper block size can greatly reduce the computational complexity with a slight peak signal-to-noise ratio (PSNR) performance loss.…”