In this paper, a compressing and reconstruction method for a noise video based on Compressed Sensing (CS) theory is proposed. At first, the CS theory is presented. Then the noise video is estimated from noisy measurement by solving the convex minimization problem. The video recovery algorithms based on gradient-based method is used to compressing and reconstructing the noise signal. And a compressive sensing algorithm with gradient-based method is proposed. At last, the performance of the proposed approach is shown and compared with some conventional algorithms. Our method can obtain best results in terms of peak signal noise ratio (PSNR) than those achieved by common methods with only a little runtime.
In this paper, a denoise approach is proposed to reduce the speckle noise in SAR images
based on compress sensing. Through the skill of compressed sensing, we divide the image into some
blocks, and propose an image reconstruction method based on block compressing sensing with
Orthogonal Matching Pursuit. By adding some simulated speckle noise in the SAR image, the
performance of the proposed approach is shown and compared with a conventional algorithm. the
result has been shown that our method can get better result in terms of peak signal noise ratio (PSNR).