Color Remote Sensing Image Restoration through Singular-Spectra-Derived Self-Similarity Metrics
Color remote sensing images have key features of pronounced internal similarity characterized by numerous repetitive local patterns, so the capacity to effectively harness these self-similarity features plays a key role in the enhancement of color images. The main novelty of this study lies in that...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/22/4685 |
| _version_ | 1797459495060766720 |
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| author | Xudong Xu Zhihua Zhang M. James C. Crabbe |
| author_facet | Xudong Xu Zhihua Zhang M. James C. Crabbe |
| author_sort | Xudong Xu |
| collection | DOAJ |
| description | Color remote sensing images have key features of pronounced internal similarity characterized by numerous repetitive local patterns, so the capacity to effectively harness these self-similarity features plays a key role in the enhancement of color images. The main novelty of this study lies in that we utilized an unusual technique (singular spectrum) to derive brand-new similarity metrics inside the quaternion representation of color images and then incorporated these metrics into denoising algorithms. Color image denoising experiments demonstrated that compared with seven mainstream image restoration algorithms (homomorphic filtering (HPF), wavelet transforms (WT), non-local means (NLM), non-local total variation (NLTV), the color adaptation of non-local means (NLMC), quaternion Euclidean metric (QNLM), and quaternion Euclidean metric total variation (QNLTV)), our algorithms with two novel self-similarity metrics achieved maximum peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), average gradient (AG), and information entropy index (IE) values, with average increases of 1.98 dB /2.12 dB, 0.1168/0.1244, 1.824/1.897, and 0.158/0.135. Moreover, for a complex, mixed-noise scenario, two versions of our algorithms also achieved average increases of 0.382 dB/0.394 dB and 0.0207/0.0210 under Motion and Gaussian mixed noise and average increases of 0.129 dB/0.154 dB and 0.0154/0.0158 under Average and Gaussian mixed noise compared with three quaternion-based restoration algorithms (QNLM, QNLTV, and quantization weighted nuclear norm minimization (QWNNM)). |
| first_indexed | 2024-03-09T16:53:09Z |
| format | Article |
| id | doaj.art-d3c80daea79f49c59aa9f009410dc9f7 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T16:53:09Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d3c80daea79f49c59aa9f009410dc9f72023-11-24T14:39:41ZengMDPI AGElectronics2079-92922023-11-011222468510.3390/electronics12224685Color Remote Sensing Image Restoration through Singular-Spectra-Derived Self-Similarity MetricsXudong Xu0Zhihua Zhang1M. James C. Crabbe2School of Mathematics, Shandong University, Jinan 250100, ChinaSchool of Mathematics, Shandong University, Jinan 250100, ChinaWolfson College, Oxford University, Oxford OX2 6UD, UKColor remote sensing images have key features of pronounced internal similarity characterized by numerous repetitive local patterns, so the capacity to effectively harness these self-similarity features plays a key role in the enhancement of color images. The main novelty of this study lies in that we utilized an unusual technique (singular spectrum) to derive brand-new similarity metrics inside the quaternion representation of color images and then incorporated these metrics into denoising algorithms. Color image denoising experiments demonstrated that compared with seven mainstream image restoration algorithms (homomorphic filtering (HPF), wavelet transforms (WT), non-local means (NLM), non-local total variation (NLTV), the color adaptation of non-local means (NLMC), quaternion Euclidean metric (QNLM), and quaternion Euclidean metric total variation (QNLTV)), our algorithms with two novel self-similarity metrics achieved maximum peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), average gradient (AG), and information entropy index (IE) values, with average increases of 1.98 dB /2.12 dB, 0.1168/0.1244, 1.824/1.897, and 0.158/0.135. Moreover, for a complex, mixed-noise scenario, two versions of our algorithms also achieved average increases of 0.382 dB/0.394 dB and 0.0207/0.0210 under Motion and Gaussian mixed noise and average increases of 0.129 dB/0.154 dB and 0.0154/0.0158 under Average and Gaussian mixed noise compared with three quaternion-based restoration algorithms (QNLM, QNLTV, and quantization weighted nuclear norm minimization (QWNNM)).https://www.mdpi.com/2079-9292/12/22/4685color imagesdenoisingsingular spectraself-similarity metrics |
| spellingShingle | Xudong Xu Zhihua Zhang M. James C. Crabbe Color Remote Sensing Image Restoration through Singular-Spectra-Derived Self-Similarity Metrics Electronics color images denoising singular spectra self-similarity metrics |
| title | Color Remote Sensing Image Restoration through Singular-Spectra-Derived Self-Similarity Metrics |
| title_full | Color Remote Sensing Image Restoration through Singular-Spectra-Derived Self-Similarity Metrics |
| title_fullStr | Color Remote Sensing Image Restoration through Singular-Spectra-Derived Self-Similarity Metrics |
| title_full_unstemmed | Color Remote Sensing Image Restoration through Singular-Spectra-Derived Self-Similarity Metrics |
| title_short | Color Remote Sensing Image Restoration through Singular-Spectra-Derived Self-Similarity Metrics |
| title_sort | color remote sensing image restoration through singular spectra derived self similarity metrics |
| topic | color images denoising singular spectra self-similarity metrics |
| url | https://www.mdpi.com/2079-9292/12/22/4685 |
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