Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior release_42vmcbvvqrfipnvuxdscuuuzly

by Lina Zhuang, Michael NG, Xiyou Fu

Published in Remote Sensing by MDPI AG.

2021   Volume 13, Issue 20, p4098

Abstract

The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements. The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques. This work aims to estimate clean HSIs from observations corrupted by mixed noise (containing Gaussian noise, impulse noise, and dead-lines/stripes) by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. We take advantage of the spectral low-rankness of HSIs by representing spectral vectors in an orthogonal subspace, which is learned from observed images by a new method. Subspace representation coefficients of HSIs are learned by solving an optimization problem plugged with an image prior extracted from a neural denoising network. The proposed method is evaluated on simulated and real HSIs. An exhaustive array of experiments and comparisons with state-of-the-art denoisers were carried out.
In application/xml+jats format

Archived Files and Locations

application/pdf  10.8 MB
file_r3nwznz6ajbhvdmhy52vaukq4a
mdpi-res.com (publisher)
web.archive.org (webarchive)
application/pdf  10.8 MB
file_frp3xjcuora5fdxsm3xnqjsjke
mdpi-res.com (web)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2021-10-13
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  2072-4292
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: eef03683-c6fd-423b-ac73-921c5123029b
API URL: JSON