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International Journal of Computer Vision

, Volume 122, Issue 2, pp 228–245 | Cite as

Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Restoration

  • Ying Fu
  • Antony Lam
  • Imari Sato
  • Yoichi Sato
Article

Abstract

Hyperspectral imaging is beneficial in a diverse range of applications from diagnostic medicine, to agriculture, to surveillance to name a few. However, hyperspectral images often suffer from degradation such as noise and low resolution. In this paper, we propose an effective model for hyperspectral image (HSI) restoration, specifically image denoising and super-resolution. Our model considers three underlying characteristics of HSIs: sparsity across the spatial-spectral domain, high correlation across spectra, and non-local self-similarity over space. We first exploit high correlation across spectra and non-local self-similarity over space in the degraded HSI to learn an adaptive spatial-spectral dictionary. Then, we employ the local and non-local sparsity of the HSI under the learned spatial-spectral dictionary to design an HSI restoration model, which can be effectively solved by an iterative numerical algorithm with parameters that are adaptively adjusted for different clusters and different noise levels. In experiments on HSI denoising, we show that the proposed method outperforms many state-of-the-art methods under several comprehensive quantitative assessments. We also show that our method performs well on HSI super-resolution.

Keywords

Adaptive spatial-spectral dictionary learning Hyperspectral image restoration Self-similarity High correlation across spectra Non-local sparse representation 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Institute of Industrial ScienceThe University of TokyoTokyoJapan
  2. 2.Graduate School of Science and EngineeringSaitama UniversitySaitamaJapan
  3. 3.National Institute of InformaticsTokyoJapan

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