Perceptual compressive sensing for image signals

Human eyes have different sensitivity to different frequency components of image signals, typically, low frequency components are relatively more crucial to the perceptual quality of images than high frequency components. Based on this observation, we propose a novel sampling scheme for compressive sensing framework by designing a weighting scheme for the sampling matrix. By adjusting the weighting coefficients, we can tune the structure of the sampling matrix to favor the frequency components that are important to human perception, so that those components could be more precisely recovered in the reconstruction procedure. Experimental results reveal that our proposed scheme can greatly enhance the performance of compressive sensing framework in both PSNR and visual quality without increasing the complexity of the framework structure or computational procedure.