Learning spectral-spatial prior via 3DDNCNN for hyperspectral image deconvolution

Xiuheng Wang, Jie Chen, Cédric Richard, David Brie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2403-2407
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • 3D convolution
  • ADMM
  • Deep learning
  • Hyperspectral image deconvolution
  • Spectral-spatial prior

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