A Huber Function based Restoration Algorithm for Astronomy Image Compression

Abstract

A new restoration algorithm based on Huber function for astronomy image compression was proposed in this paper. A combinatorial sensing matrix based on noiselet transform and subsample matrix was built for image acquisition. A restoration algorithm based on Huber function was introduced to reconstruct the signal. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were used to evaluate the performance of the proposed algorithm. Compared with the standard compression algorithms, including JPEG and iterative shrinkage thresholding algorithm (ISTA) based reconstruction algorithm, the results obtained by the proposed algorithm are of higher structural similarity and PSNR. The proposed algorithm is suitable for the application scenarios of astronomy images with large data volume and high redundancy.