An Improved Patch-Group-Based Sparse Representation Method for Image Compressive Sensing

Abstract

Compressive sensing (CS) is widely being used in recent times for image compression and transmission. The major challenge in CS is perfect reconstruction of the original image from fewest samples with fast convergence. Dictionary learning and the wide-ranging optimization problem give rise to high computation complexity. Group sparse representation (GSR)-based approach is one of the fast and efficient methods of CS image recovery. However, it may tend to oversmoothen the recovered image. This research work focuses on enhancing the performance of GSR-based methods. In this research work, both local and non-local image patches are exploited in two phases of reconstruction. In the initialization phase, adaptive sparsifying l0 optimization model is utilized for local overlapping patches. Further, constrained GSR is applied on the outcome. The split Bregman iteration is harnessed to solve the resultant patch-group model. The execution time, PSNR and FSIM are employed as the evaluation parameters for evaluating the CS performance. The parametric performance is tested over a set of standard images. The experimental results show the competence of the proposed patch-group method over the existing ALSB, GSR and GSR-NCR (GSR with non-convex regularization) methods. The proposed model offers 1.3, 0.32 and 0.51 dB average PSNR gain over ALSB, GSR and GSR-NCR methods, respectively. The average execution time taken by the proposed work is 29% less than the traditional GSR method.