File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/JSTARS.2020.2979801
- Scopus: eid_2-s2.0-85083452049
- WOS: WOS:000527687800001
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Hyperspectral Mixed Noise Removal By ℓ1-Norm-Based Subspace Representation
Title | Hyperspectral Mixed Noise Removal By ℓ<font size=-1><sub>1</sub></font>-Norm-Based Subspace Representation |
---|---|
Authors | |
Keywords | Gaussian noise Hyperspectral imaging Transforms Noise reduction Additives |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443 |
Citation | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, v. 13, p. 1143-1157 How to Cite? |
Abstract | This article introduces a new hyperspectral image (HSI) denoising method that is able to cope with additive mixed noise, i.e., mixture of Gaussian noise, impulse noise, and stripes, which usually corrupt hyperspectral images in the acquisition process. The proposed method fully exploits a compact and sparse HSI representation based on its low-rank and self-similarity characteristics. In order to deal with mixed noise having a complex statistical distribution, we propose to use the robust ℓ 1 data fidelity instead of using the ℓ 1 data fidelity, which is commonly employed for Gaussian noise removal. In a series of experiments with simulated and real datasets, the proposed method competes with state-of-the-art methods, yielding better results for mixed noise removal. |
Persistent Identifier | http://hdl.handle.net/10722/288098 |
ISSN | 2023 Impact Factor: 4.7 2023 SCImago Journal Rankings: 1.434 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhuang, L | - |
dc.contributor.author | Ng, MK | - |
dc.date.accessioned | 2020-10-05T12:07:50Z | - |
dc.date.available | 2020-10-05T12:07:50Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, v. 13, p. 1143-1157 | - |
dc.identifier.issn | 1939-1404 | - |
dc.identifier.uri | http://hdl.handle.net/10722/288098 | - |
dc.description.abstract | This article introduces a new hyperspectral image (HSI) denoising method that is able to cope with additive mixed noise, i.e., mixture of Gaussian noise, impulse noise, and stripes, which usually corrupt hyperspectral images in the acquisition process. The proposed method fully exploits a compact and sparse HSI representation based on its low-rank and self-similarity characteristics. In order to deal with mixed noise having a complex statistical distribution, we propose to use the robust ℓ 1 data fidelity instead of using the ℓ 1 data fidelity, which is commonly employed for Gaussian noise removal. In a series of experiments with simulated and real datasets, the proposed method competes with state-of-the-art methods, yielding better results for mixed noise removal. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443 | - |
dc.relation.ispartof | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Gaussian noise | - |
dc.subject | Hyperspectral imaging | - |
dc.subject | Transforms | - |
dc.subject | Noise reduction | - |
dc.subject | Additives | - |
dc.title | Hyperspectral Mixed Noise Removal By ℓ<font size=-1><sub>1</sub></font>-Norm-Based Subspace Representation | - |
dc.type | Article | - |
dc.identifier.email | Ng, MK: michael.ng@hku.hk | - |
dc.identifier.authority | Ng, MK=rp02578 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/JSTARS.2020.2979801 | - |
dc.identifier.scopus | eid_2-s2.0-85083452049 | - |
dc.identifier.hkuros | 315736 | - |
dc.identifier.volume | 13 | - |
dc.identifier.spage | 1143 | - |
dc.identifier.epage | 1157 | - |
dc.identifier.isi | WOS:000527687800001 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1939-1404 | - |