A Novel Compressive Sensing Spatially Adaptive Total Variation Method for High Noise Astronomical Image Denoising

High-noise astronomical-image denoising has always been a research hotspot in deep space exploration. Compressive sensing (CS) is an advanced technology used for high-dimensional signal processing. It is useful for processing high-resolution astronomical images. To obtain high-quality astronomical images, a CS spatially adaptive total variation iterative (CSSATVI) method is proposed herein. In this method, a curvelet transform based on an adaptive curvelet soft thresholding operator is proposed to adaptively remove hidden noise information in the process of image sparse representation, and a novel CS denoising reconstruction model proposed is used to deeply mine the texture, edge and other detailed information. Moreover, a novel reconstruction strategy is proposed for preserving detailed image information in the iterative reconstruction process to obtain high-quality astronomical images. Simulation results indicated that the proposed CSSATVI method can quickly reconstruct a high-quality astronomical image and preserve a large amount of astronomical image details; thus, it can be effectively applied in deep space exploration.