A Novel CS 2G-starlet denoising method for high noise astronomical image

In deep space exploration, astronomical images are easily contaminated by noise, which affects image quality. However, for the denoising of high-noise astronomical images, obtaining good reconstruction results from the current denoising methods is difficult. This paper proposes a novel compressed sensing (CS) 2G-starlet denoising (NCSSD) method can effectively improve the reconstruction quality of astronomical images. Firstly, the proposed 2G-starlet Wiener filtering operator is used to select these starlet coefficients in the process of sparse image transformation. Then, a novel iterative stepsize operator is proposed to enhance the convergence ability of the algorithm. At the same time, an optimized cycle spinning method is proposed to adjust the reconstructed image. Through extensive simulation experiments and deep theoretical analysis and verification, the effectiveness of this method in high-noise astronomical image denoising is fully proven, and the quality of reconstructed image is improved. The proposed method has been successfully interconnected with the telescope and industrial camera, realizing the rapid acquisition of high-quality astronomical images. The research results of this article have important value for promoting the effective acquisition and analysis of astronomical images in exploring deep space.