Abstract
Hyperspectral image (HSI) denoising is an important tool to improve the quality of HSIs for subsequent tasks. In this paper, we propose a novel method based on Residual Multiresolution Network (RMRNet) for HSI denoising, which exploits multiscale information better from multiresolution versions of HSIs produced by pixelshuffle operation. The convolutional neural network (CNN) is used for extracting the spatial information among different resolution HSI, respectively. Enhanced representation will be obtained by fusing these multiresolution features. Wide receptive fields are provided by dilated convolution. Spectral information is also considered in the proposed network. To ease the flow of low-frequency information, we use a residual structure in our method. In addition, the experiment results on the simulated dataset demonstrate the superiority of our RMRNet.
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Xiu, S., Gao, F., Chen, Y. (2021). Residual Multi-resolution Network for Hyperspectral Image Denoising. In: Wang, Y., Song, W. (eds) Image and Graphics Technologies and Applications. IGTA 2021. Communications in Computer and Information Science, vol 1480. Springer, Singapore. https://doi.org/10.1007/978-981-16-7189-0_1
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