Compressed Sensing for Astronomical Image Compression and Denoising

In deep exploration, astronomical images are often contaminated by noise when they are transmitted to the earth by satellites. In addition, the existing compression methods are difficult to compress the image with a lower compression sampling ratio, resulting in longer image data transmission time. Donoho proposed a new sampling theory named compressed sensing (CS) in 2006, which can recover a high quality image only using a few information of original image. In this paper, CS method is employed to solve the problem of astronomical image compression, meanwhile, the CS recovery denoising algorithm based curvelet is proposed for astronomical image denoising. The experimental results show that the compression performance of CS method is superior to the famous JPEG and JPEG-2000 compression method, it can compress the high resolution astronomical image with a lower compression sampling ratio. Meanwhile, the proposed algorithm can effectively remove more noise from the noisy image, and preserves more detailed features.