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Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising by Pan, Haodong; Gao, Feng; Dong, Junyu; Du, Qian is a scholarly article available to read on EtoBox.

What is Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising about?

Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global or spectral context information for HSI denoising. However, existing methods still have limitations in feature interaction exploitation among multiple scales and rich spectral structure preservation. In view of this, we propose a novel solution to investigate the HSI denoising using a Multi-scale Adaptive Fusion Network (MAFNet), which can learn the complex nonlinear mapping between clean and noisy HSI. Two key components contribute to improving the hyperspectral image denoising: A progressively multiscale information aggregation network and a co-attention fusion module. Specifically, we first generate a set of multiscale images and feed them into a coarse-fusion network to exploit the contextual texture correlation. Thereafter, a fine fusion network is followed to exchange the information across the parallel multiscale subnetworks. Furthermore, we design a co-attention fusion module to adaptively emphasize informative features from different scales, and thereby enhance the discriminative learning capabili

Author

Pan, Haodong; Gao, Feng; Dong, Junyu; Du, Qian

Published

2023

Language

EN