Progressive Recurrent Neural Network for Multispectral Remote Sensing Image Destriping

An unstable imaging system often introduces additional stripe noise in multispectral remote sensing images during the data acquisition process given a variety of factors. The complicated stripe distributions lead to the residual stripe in the results of existing methods, thus increasing the difficulty of destriping in practice. Mainstream deep-learning-based methods show the encouraging destriping performance on multispectral remote sensing images. However, they often require the model to handle the varying degrees of stripe noise in a single shot for each image, which results in the poor destriping performance when facing practical cases with diverse stripe distributions. To address the above issue, we propose a progressive recurrent neural network (PRNet) to remove the stripe noise for each degraded image in an iterative manner. More specifically, a progressive destriping strategy is designed to gradually restore the clean image, in which the main recurrent module (MRM) is introduced to iteratively process the stripe removal results generated from previous timesteps until the clean image is obtained. Furthermore, since the uniformity of the entire image is supposed to be significantly enhanced after destriping, it is necessary to take the local spatial correlation into account during destriping. Therefore, we present the patch-based sequence module (PSM) to leverage the local spatial correlation by splitting the image into multiscale patch sequences and capturing the relationship among different patches. Extensive experimental results on different datasets demonstrate that the proposed model yields superior destriping performance compared with other methods, especially for removing the stripe noise with complex distributions.