A Precise Multi-Exposure Image Fusion Method Based on Low-level Features
Multi exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/6/1597 |
| _version_ | 1811299419736244224 |
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| author | Guanqiu Qi Liang Chang Yaqin Luo Yinong Chen Zhiqin Zhu Shujuan Wang |
| author_facet | Guanqiu Qi Liang Chang Yaqin Luo Yinong Chen Zhiqin Zhu Shujuan Wang |
| author_sort | Guanqiu Qi |
| collection | DOAJ |
| description | Multi exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes a precise MEF method based on feature patches (FPM) to improve the robustness of ghost removal in a dynamic scene. A reference image is selected by a priori exposure quality first and then used in the structure consistency test to solve the image ghosting issues existing in the dynamic scene MEF. Source images are decomposed into spatial-domain structures by a guided filter. Both the base and detail layer of the decomposed images are fused to achieve the MEF. The structure decomposition of the image patch and the appropriate exposure evaluation are integrated into the proposed solution. Both global and local exposures are optimized to improve the fusion performance. Compared with six existing MEF methods, the proposed FPM not only improves the robustness of ghost removal in a dynamic scene, but also performs well in color saturation, image sharpness, and local detail processing. |
| first_indexed | 2024-04-13T06:34:46Z |
| format | Article |
| id | doaj.art-73c8e2ba2109462d81429dd960458f8c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:34:46Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-73c8e2ba2109462d81429dd960458f8c2022-12-22T02:57:57ZengMDPI AGSensors1424-82202020-03-01206159710.3390/s20061597s20061597A Precise Multi-Exposure Image Fusion Method Based on Low-level FeaturesGuanqiu Qi0Liang Chang1Yaqin Luo2Yinong Chen3Zhiqin Zhu4Shujuan Wang5Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USACollege of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaCollege of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287, USACollege of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, ChinaMulti exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes a precise MEF method based on feature patches (FPM) to improve the robustness of ghost removal in a dynamic scene. A reference image is selected by a priori exposure quality first and then used in the structure consistency test to solve the image ghosting issues existing in the dynamic scene MEF. Source images are decomposed into spatial-domain structures by a guided filter. Both the base and detail layer of the decomposed images are fused to achieve the MEF. The structure decomposition of the image patch and the appropriate exposure evaluation are integrated into the proposed solution. Both global and local exposures are optimized to improve the fusion performance. Compared with six existing MEF methods, the proposed FPM not only improves the robustness of ghost removal in a dynamic scene, but also performs well in color saturation, image sharpness, and local detail processing.https://www.mdpi.com/1424-8220/20/6/1597multi-exposure image fusionhigh-dynamic-range imagingghost removalimage fusiona priori exposure quality |
| spellingShingle | Guanqiu Qi Liang Chang Yaqin Luo Yinong Chen Zhiqin Zhu Shujuan Wang A Precise Multi-Exposure Image Fusion Method Based on Low-level Features Sensors multi-exposure image fusion high-dynamic-range imaging ghost removal image fusion a priori exposure quality |
| title | A Precise Multi-Exposure Image Fusion Method Based on Low-level Features |
| title_full | A Precise Multi-Exposure Image Fusion Method Based on Low-level Features |
| title_fullStr | A Precise Multi-Exposure Image Fusion Method Based on Low-level Features |
| title_full_unstemmed | A Precise Multi-Exposure Image Fusion Method Based on Low-level Features |
| title_short | A Precise Multi-Exposure Image Fusion Method Based on Low-level Features |
| title_sort | precise multi exposure image fusion method based on low level features |
| topic | multi-exposure image fusion high-dynamic-range imaging ghost removal image fusion a priori exposure quality |
| url | https://www.mdpi.com/1424-8220/20/6/1597 |
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