Fast stripe noise removal from hyperspectral image via multi-scale dilated unidirectional convolution

Hyperspectral images (HSIs) are often contaminated by noises due to the multi-detector imaging systems, which greatly affects the subsequent HSIs interpretation and application. The 3D HSIs deliver extra spectral information, which makes the most existing destriping algorithms hardly satisfied, and the complete stripes removal and less test time consuming remain to be overcome. To meet these challenges, we present a multi-scale dilated unidirectional convolution network (MsDUC) with the following contributions. First, the deep learning-based method can fully exploit and preserve spatial-spectral correlations in 3D HSIs while the conventional methods failed to realize it. Second, different dilated convolution learns different scale features, so the introduced multi-scale dilated convolution could get more contextual information for the final restoration. Third, the clear directional signature of stripe noise and the unidirectional total variation (UTV) model inspired us to put forward the unidirectional convolution to capture the directional signature of stripe, meanwhile, the less trainable parameters and the utilized residual strategy speed up the learning process. Experimental results have shown that our method outperforms many of the state-of-the-art methods in both image restoration performance and test running time. Our code can be download from https://github.com/doctorwgd/MsDUC .