SLO Compressive Sensing Signal Recovery Algorithm And Application On Image Recover

Compressed sensing has brought new ideas to the signal processing. Through the theory of compressed sensing, can accurately restore the signal at the sampling signal is far lower than the Nyquist sampling rate of. The information we need is huge, if the sampling signal through traditional signal processing method, takes the signal bandwidth greatly, resulting in waste of bandwidth. Compressed sensing is a good solution for Nyquist sampling rate of traditional signal processing problems. At present, the compressed sensing has come from an emerging field of widely used in speech, image, video signal processing.Image reconstruction is a very important part in the field of signal processing. Image signal due to the correlation between each pixel, which makes the image reconstruction and restoration algorithm is a simple extension of one-dimensional signal, also need to consider the factors of special image reconstruction.Based on the theory of perception signal recovery algorithm study on compression, focuses on the study of SLO compressed sensing signal recovery algorithm. And make the improvement to the deficiency of SLO compressed sensing signal recovery algorithm. And the improved SLO algorithm combining image block compressed sensing algorithm is applied to image reconstruction in. SLO algorithm is used in the Gauss function as the 0 norm estimation function, using the steepest descent method and the algorithm in finding the minimum value of the process will appear jagged effect. Shrinkage in the compressed sensing and the minimization problem is a more common method, in the process of doing algorithm SLO algorithm, threshold shrinkage algorithm is introduced into the SLO algorithm, to speed up the algorithm speed of recovery algorithm, reduce recovery time. Compressed sensing image block reconstruction algorithm for image signal while removing block effect of image, but the image quality is reduced. The improved algorithm is applied to the sub block image compressed sensing algorithm, image recovery time, number of iterations decreases, the picture quality of the improved reconstruction.