A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure
Multi-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by th...
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| Format: | Article |
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MDPI AG
2018-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/20/12/935 |
| _version_ | 1797999470570373120 |
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| author | Yuanyuan Li Yanjing Sun Mingyao Zheng Xinghua Huang Guanqiu Qi Hexu Hu Zhiqin Zhu |
| author_facet | Yuanyuan Li Yanjing Sun Mingyao Zheng Xinghua Huang Guanqiu Qi Hexu Hu Zhiqin Zhu |
| author_sort | Yuanyuan Li |
| collection | DOAJ |
| description | Multi-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by the human eye. This paper proposes a novel multi-exposure image fusion (MEF) method based on adaptive patch structure. The proposed algorithm combines image cartoon-texture decomposition, image patch structure decomposition, and the structural similarity index to improve the local contrast of the image. Moreover, the proposed method can capture more detailed information of source images and produce more vivid high-dynamic-range (HDR) images. Specifically, image texture entropy values are used to evaluate image local information for adaptive selection of image patch size. The intermediate fused image is obtained by the proposed structure patch decomposition algorithm. Finally, the intermediate fused image is optimized by using the structural similarity index to obtain the final fused HDR image. The results of comparative experiments show that the proposed method can obtain high-quality HDR images with better visual effects and more detailed information. |
| first_indexed | 2024-04-11T11:05:10Z |
| format | Article |
| id | doaj.art-6d0d55a6bacd418db9dde1f4e9a07953 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T11:05:10Z |
| publishDate | 2018-12-01 |
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| series | Entropy |
| spelling | doaj.art-6d0d55a6bacd418db9dde1f4e9a079532022-12-22T04:28:24ZengMDPI AGEntropy1099-43002018-12-01201293510.3390/e20120935e20120935A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch StructureYuanyuan Li0Yanjing Sun1Mingyao Zheng2Xinghua Huang3Guanqiu Qi4Hexu Hu5Zhiqin Zhu6School of Information and Electrical, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Electrical, China University of Mining and Technology, Xuzhou 221116, ChinaCollege of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaCollege of Automation, Chongqing University, Chongqing 400044, ChinaDepartment of Mathematics and Computer Information Science, Mansfield University of Pennsylvania, Mansfield, PA 16933, USACollege of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaCollege of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaMulti-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by the human eye. This paper proposes a novel multi-exposure image fusion (MEF) method based on adaptive patch structure. The proposed algorithm combines image cartoon-texture decomposition, image patch structure decomposition, and the structural similarity index to improve the local contrast of the image. Moreover, the proposed method can capture more detailed information of source images and produce more vivid high-dynamic-range (HDR) images. Specifically, image texture entropy values are used to evaluate image local information for adaptive selection of image patch size. The intermediate fused image is obtained by the proposed structure patch decomposition algorithm. Finally, the intermediate fused image is optimized by using the structural similarity index to obtain the final fused HDR image. The results of comparative experiments show that the proposed method can obtain high-quality HDR images with better visual effects and more detailed information.https://www.mdpi.com/1099-4300/20/12/935multi-exposure image fusiontexture information entropyadaptive selectionpatch structure decomposition |
| spellingShingle | Yuanyuan Li Yanjing Sun Mingyao Zheng Xinghua Huang Guanqiu Qi Hexu Hu Zhiqin Zhu A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure Entropy multi-exposure image fusion texture information entropy adaptive selection patch structure decomposition |
| title | A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure |
| title_full | A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure |
| title_fullStr | A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure |
| title_full_unstemmed | A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure |
| title_short | A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure |
| title_sort | novel multi exposure image fusion method based on adaptive patch structure |
| topic | multi-exposure image fusion texture information entropy adaptive selection patch structure decomposition |
| url | https://www.mdpi.com/1099-4300/20/12/935 |
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