Hyperspectral Image Fusion Using Fast High-Dimensional Denoising

Nair, P and Unni, VS and Chaudhury, KN (2019) Hyperspectral Image Fusion Using Fast High-Dimensional Denoising. In: 26th IEEE International Conference on Image Processing, ICIP 2019, 22 - 25 September 2019, Taipei, pp. 3123-3127.

PDF ICIP_2019.pdf - Published Version Restricted to Registered users only Download (723kB) | Request a copy

Abstract

In hyperspectral image fusion, a high resolution multispectral (MS) image is combined with a low resolution hyperspectral (HS) image to obtain a high resolution HS image. In this work, we propose a 'plug-and-play' framework for HS-MS fusion, where the inversion step at each iteration involves the solution of a linear system, and the regularization is performed using a high-dimensional kernel denoiser. The core contribution is the design of the denoiser, which can denoise an HS-image at low complexity using clustering and convolutions. In particular, it can exploit the inter-band correlations, which cannot be done using band-by-band denoising. An important technical aspect of our denoiser is that it can be expressed as the proximal map of a proper, closed, and convex regularizer, which guarantees the convergence of the plug-and-play iterations. Preliminary results suggest that we are competitive with state-of-the-art algorithms for HS-MS fusion in terms of speed and restoration accuracy. © 2019 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - International Conference on Image Processing, ICIP
Publisher:	IEEE Computer Society
Additional Information:	The copyright for this article belongs to IEEE Computer Society
Keywords:	high-dimensional denoiser; hyperspectral image fusion; plug-and-play; regularization
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	06 Jan 2023 06:38
Last Modified:	06 Jan 2023 06:38
URI:	https://eprints.iisc.ac.in/id/eprint/78812

Actions (login required)

View Item