Mean Shift: A Method for Measurement Matrix of Compressive Sensing

In this work, we propose a mean shift based measurement matrix for compressive sensing and systematically investigate the possibility of constructing measurement matrix with mean shift of different chaotic sequences. With this matrix, we apply it in compressive sensing of digital images and compare the accuracy of reconstruction while using it to construct measurement matrices. The experimental results showed that mean shift based measurement matrix for compressive sensing can not only lead to visible PSNR improvements over state-of the-art method such as Gaussian random matrix method, but also preserve much better the image structures when compressed and generate good recovered visual quality.