Single-Image Simultaneous Destriping and Denoising: Double Low-Rank Property
When a remote sensing camera work in push-broom mode, the obtained image usually contains significant stripe noise and random noise due to differences in detector response and environmental factors. Traditional approaches typically treat them as two independent problems and process the image sequent...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/24/5710 |
| _version_ | 1797379471100084224 |
|---|---|
| author | Xiaobin Wu Liangliang Zheng Chunyu Liu Tan Gao Ziyu Zhang Biao Yang |
| author_facet | Xiaobin Wu Liangliang Zheng Chunyu Liu Tan Gao Ziyu Zhang Biao Yang |
| author_sort | Xiaobin Wu |
| collection | DOAJ |
| description | When a remote sensing camera work in push-broom mode, the obtained image usually contains significant stripe noise and random noise due to differences in detector response and environmental factors. Traditional approaches typically treat them as two independent problems and process the image sequentially, which not only increases the risk of information loss and structural damage, but also faces the situation of noise mutual influence. To overcome the drawbacks of traditional methods, this paper leverages the double low-rank characteristics in the underlying prior of degraded images and presents a novel approach for addressing both destriping and denoising tasks simultaneously. We utilize the commonality that both can be treated as inverse problems and place them in the same optimization framework, while designing an alternating direction method of multipliers (ADMM) strategy for solving them, achieving the synchronous removal of both stripe noise and random noise. Compared with traditional approaches, synchronous denoising technology can more accurately evaluate the distribution characteristics of noise, better utilize the original information of the image, and achieve better destriping and denoising results. To assess the efficacy of the proposed algorithm, extensive simulations and experiments were conducted in this paper. The results show that compared with state-of-the-art algorithms, the proposed method can more effectively suppress random noise, achieve better synchronous denoising results, and it exhibits a stronger robustness. |
| first_indexed | 2024-03-08T20:23:42Z |
| format | Article |
| id | doaj.art-0ef507a60b0a4962bc27def9ca1a8824 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T20:23:42Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0ef507a60b0a4962bc27def9ca1a88242023-12-22T14:39:06ZengMDPI AGRemote Sensing2072-42922023-12-011524571010.3390/rs15245710Single-Image Simultaneous Destriping and Denoising: Double Low-Rank PropertyXiaobin Wu0Liangliang Zheng1Chunyu Liu2Tan Gao3Ziyu Zhang4Biao Yang5Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaWhen a remote sensing camera work in push-broom mode, the obtained image usually contains significant stripe noise and random noise due to differences in detector response and environmental factors. Traditional approaches typically treat them as two independent problems and process the image sequentially, which not only increases the risk of information loss and structural damage, but also faces the situation of noise mutual influence. To overcome the drawbacks of traditional methods, this paper leverages the double low-rank characteristics in the underlying prior of degraded images and presents a novel approach for addressing both destriping and denoising tasks simultaneously. We utilize the commonality that both can be treated as inverse problems and place them in the same optimization framework, while designing an alternating direction method of multipliers (ADMM) strategy for solving them, achieving the synchronous removal of both stripe noise and random noise. Compared with traditional approaches, synchronous denoising technology can more accurately evaluate the distribution characteristics of noise, better utilize the original information of the image, and achieve better destriping and denoising results. To assess the efficacy of the proposed algorithm, extensive simulations and experiments were conducted in this paper. The results show that compared with state-of-the-art algorithms, the proposed method can more effectively suppress random noise, achieve better synchronous denoising results, and it exhibits a stronger robustness.https://www.mdpi.com/2072-4292/15/24/5710denoisingdestripinglow rankremote sensing |
| spellingShingle | Xiaobin Wu Liangliang Zheng Chunyu Liu Tan Gao Ziyu Zhang Biao Yang Single-Image Simultaneous Destriping and Denoising: Double Low-Rank Property Remote Sensing denoising destriping low rank remote sensing |
| title | Single-Image Simultaneous Destriping and Denoising: Double Low-Rank Property |
| title_full | Single-Image Simultaneous Destriping and Denoising: Double Low-Rank Property |
| title_fullStr | Single-Image Simultaneous Destriping and Denoising: Double Low-Rank Property |
| title_full_unstemmed | Single-Image Simultaneous Destriping and Denoising: Double Low-Rank Property |
| title_short | Single-Image Simultaneous Destriping and Denoising: Double Low-Rank Property |
| title_sort | single image simultaneous destriping and denoising double low rank property |
| topic | denoising destriping low rank remote sensing |
| url | https://www.mdpi.com/2072-4292/15/24/5710 |
| work_keys_str_mv | AT xiaobinwu singleimagesimultaneousdestripinganddenoisingdoublelowrankproperty AT liangliangzheng singleimagesimultaneousdestripinganddenoisingdoublelowrankproperty AT chunyuliu singleimagesimultaneousdestripinganddenoisingdoublelowrankproperty AT tangao singleimagesimultaneousdestripinganddenoisingdoublelowrankproperty AT ziyuzhang singleimagesimultaneousdestripinganddenoisingdoublelowrankproperty AT biaoyang singleimagesimultaneousdestripinganddenoisingdoublelowrankproperty |