Learning Spectral-Spatial Prior Via 3DDNCNN for Hyperspectral Image Deconvolution
Xiuheng Wang, Jie Chen, Cédric Richard, David Brie
Abstract
Hyperspectral image (HSI) deconvolution is an ill-posed problem aiming at recovering sharp images with tens or hundreds of spectral channels from blurred and noisy observations. In order to successfully conduct the deconvolution, proper priors are required to regularize the optimization problem. However, handcrafting a good regularizer may not be trivial and complex regularizers lead to difficulties in solving the optimization problem. In this paper, we use the alternating direction method of multipliers (ADMM) to decompose the optimization problem into iterative subproblems where the prior only appears in a denoising subproblem. Then a 3D denoising convolutional neural network (3DDnCNN) is designed and trained with data for solving this problem. In this way, the hyperspectral image deconvolution is then solved with a framework that integrates the optimization techniques and deep learning. Experimental results demonstrate the superiority of the proposed method with several blurring settings in both quantitative and qualitative comparisons.
BibTeX
@inproceedings{icassp2020_learningspectral,
title = {Learning Spectral-Spatial Prior Via 3DDNCNN for Hyperspectral Image Deconvolution},
author = {Xiuheng Wang and Jie Chen and Cédric Richard and David Brie},
booktitle = {ICASSP 2020},
year = {2020}
}