LAUSR

Multispectral remote sensing images (RSI), including hyperspectral and multispectral images, contain adequate information of ground objects and areas and play important roles in environmental monitoring, weather forecasting, urban planning, and so on. However, due to many inevitable external effects on the remote sensing pathway, RSIs are often degraded by blur. The fields of multispectral RSI deblurring have witnessed great improvements in recent years, including both optimization-based and deep-learning-based methods. However, issues are to be addressed in the RSI deblurring field, such as the incompatibility of general regularizations, lack of spectral correlations for multispectral RSIs, demands of blind deblurring for real-world applications, and high costs of computation. To address these problems, we incorporate a novel prior exploiting gradient information similarity between different spectral bands, and we name it auxiliary band gradient information (ABGI) prior. We show that the ABGI prior is applicable to all gradient sparsity regularizations by a simple subtract-then-add step. Specifically, we apply ABGI prior to the patchwise minimal pixel (PMP) prior-based deblurring method, and we also prove that the PMP prior exhibits sparsity for clear natural RSIs. We estimate our method on RSI datasets of different spectral types and geographic resolutions. Compared with other state-of-the-art deblurring methods, our method shows superior performance on both simulated and real-world blur.