Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform
This study based on co-occurrence analysis shearlet transform (CAST) effectively combines the latent low rank representation (LatLRR) and the regularization of zero-crossing counting in differences to fuse the heterogeneous images. First, the source images are decomposed by CAST method into base-lay...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/2/283 |
| _version_ | 1797490713015877632 |
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| author | Biao Qi Longxu Jin Guoning Li Yu Zhang Qiang Li Guoling Bi Wenhua Wang |
| author_facet | Biao Qi Longxu Jin Guoning Li Yu Zhang Qiang Li Guoling Bi Wenhua Wang |
| author_sort | Biao Qi |
| collection | DOAJ |
| description | This study based on co-occurrence analysis shearlet transform (CAST) effectively combines the latent low rank representation (LatLRR) and the regularization of zero-crossing counting in differences to fuse the heterogeneous images. First, the source images are decomposed by CAST method into base-layer and detail-layer sub-images. Secondly, for the base-layer components with larger-scale intensity variation, the LatLRR, is a valid method to extract the salient information from image sources, and can be applied to generate saliency map to implement the weighted fusion of base-layer images adaptively. Meanwhile, the regularization term of zero crossings in differences, which is a classic method of optimization, is designed as the regularization term to construct the fusion of detail-layer images. By this method, the gradient information concealed in the source images can be extracted as much as possible, then the fusion image owns more abundant edge information. Compared with other state-of-the-art algorithms on publicly available datasets, the quantitative and qualitative analysis of experimental results demonstrate that the proposed method outperformed in enhancing the contrast and achieving close fusion result. |
| first_indexed | 2024-03-10T00:36:58Z |
| format | Article |
| id | doaj.art-0894d7df8a3d4b69a5a641a7037a5456 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:36:58Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0894d7df8a3d4b69a5a641a7037a54562023-11-23T15:15:03ZengMDPI AGRemote Sensing2072-42922022-01-0114228310.3390/rs14020283Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet TransformBiao Qi0Longxu Jin1Guoning Li2Yu Zhang3Qiang Li4Guoling Bi5Wenhua Wang6Changchun 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, ChinaSchool of Instrument Science and Electrical Engineering, Jilin University, Changchun 130012, ChinaThis study based on co-occurrence analysis shearlet transform (CAST) effectively combines the latent low rank representation (LatLRR) and the regularization of zero-crossing counting in differences to fuse the heterogeneous images. First, the source images are decomposed by CAST method into base-layer and detail-layer sub-images. Secondly, for the base-layer components with larger-scale intensity variation, the LatLRR, is a valid method to extract the salient information from image sources, and can be applied to generate saliency map to implement the weighted fusion of base-layer images adaptively. Meanwhile, the regularization term of zero crossings in differences, which is a classic method of optimization, is designed as the regularization term to construct the fusion of detail-layer images. By this method, the gradient information concealed in the source images can be extracted as much as possible, then the fusion image owns more abundant edge information. Compared with other state-of-the-art algorithms on publicly available datasets, the quantitative and qualitative analysis of experimental results demonstrate that the proposed method outperformed in enhancing the contrast and achieving close fusion result.https://www.mdpi.com/2072-4292/14/2/283image fusionco-occurrence analysis shearlet transformlatent low-rank representationregularization of zero-crossing counting in differences |
| spellingShingle | Biao Qi Longxu Jin Guoning Li Yu Zhang Qiang Li Guoling Bi Wenhua Wang Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform Remote Sensing image fusion co-occurrence analysis shearlet transform latent low-rank representation regularization of zero-crossing counting in differences |
| title | Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform |
| title_full | Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform |
| title_fullStr | Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform |
| title_full_unstemmed | Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform |
| title_short | Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform |
| title_sort | infrared and visible image fusion based on co occurrence analysis shearlet transform |
| topic | image fusion co-occurrence analysis shearlet transform latent low-rank representation regularization of zero-crossing counting in differences |
| url | https://www.mdpi.com/2072-4292/14/2/283 |
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