Optical Remote Sensing Image Deblurring Based on Deep Unfolding
Mengyang Shi, Ziyu Gu, Yesheng Gao, Xingzhao Liu, Lin Chen · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Due to the atmospheric turbulence, defocusing, noise and other factors, the optical remote sensing image acquisition may become blurred. Therefore, it is critical of deblurring the images by algorithm. In recent years, neural network algorithms have shown excellent performance in optical re-mote sensing images deblurring. However, neural network algorithms have some limitations at the same time. They lack interpretability and need large amounts of training samples. The traditional deblurring algorithms are interpretable, but the performance is not as good as the neural network algorithms. In order to obtain an interpretable deblurring algorithm with good performance, this paper proposes a deblurring algorithm based on deep unfolding method, which is the combination of traditional algorithms and neural networks. It can achieve good performance and be interpretable at the same time. We demonstrate the effectiveness of the algorithm on remote sensing datasets with PSNR values and visual deblurring images. The experiments show the proposed algorithm has better deblurring results.