Application of compressed sensing for image compression based on optimized Toeplitz sensing matrices

Abstract In compressed sensing, the Toeplitz sensing matrices are generated by randomly drawn entries and further optimizes them with suitable optimization methods. However, during an process, state-of-the-art methods tend to lose control over structure of measurement matrices. this paper, we proposed novel approach for based on evolutionary algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO) compression image signal. Furthermore, investigated performance Basis Pursuit (BP) Orthogonal Matching (OMP) reconstruction images. The optimized GA, SA, PSO exhibit a significant reduction in mutual coherence (μ) thus improved recovery 2D images compared non-optimized result reveals that achieved more accurate results robust uniform guarantee OMP algorithm. BP shows slow On other hand, matrix fast guarantee, but at cost PSNR. retains improves sensing. Finally, experimental validate effectiveness method compression.