Flexible infrared images destriping algorithm with L1-based sparse regularization for wide-field astronomical images

Abstract

A novel infrared image destriping method is proposed from a single image. The proposed method can estimate the clean image and stripe noise image simultaneously. Firstly, we analyze the common challenges in current destriping algorithms, and then integrate an unidirectional total variation constraint to remove stripe noise effectively. Wavelet transform is introduced to constrain the spatial mixture Gaussian noise in the degraded images. We employ the total variation regularization to smooth the distribution of Wavelet coefficients. Namely, the distribution of Wavelet coefficients of clean image is sparser than that of degraded stripe image. Finally, an efficient optimization strategy is described to alternately estimate the clean image and stripe image until convergence. Compared experiments are carried out on the simulated and real infrared images with the state-of-the-art models. The proposed method is able to recover high resolution infrared image in low computation cost.