Astronomical image denoising with compressed sensing and curvelet

In astronomical image denoising, to improve denoising construction performance of iterative curvelet threshold (ICT)algorithm, a compressed sensing iterative reconstruction algorithm by combining cycle spinning and curvelet wiener filtering was proposed. Firstly, cycle spinning method based on curvelet threshold was used to adjust reconstructed images for inhibiting Pseudo-gibbs effect of reconstructed images; then, proposed curvelet wiener filtering operators were used to replace wavelet threshold for sieving image curvelet coefficient to further improve the quality of reconstructed image. The reconstruction experiment on Lena image and moon image with Gaussian white noise was conducted, and the result shows that compared with traditional compressed sensing ICT algorithm, the peak signal noise ratio of proposed algorithm increases by 2.6~3.2 dB approximately. So the proposed method can acquire better denoising performance, and can protect detail information of astronomical images effectively.