A self-adaptive single underwater image restoration algorithm for improving graphic quality
Abstract A high-quality underwater image is essential to many industrial and academic applications in the field of image processing and analysis. Unfortunately, underwater images frequently demonstrate poor visual quality of low contrast, blurring, darkness, and color diminishing. This paper develop...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-09-01
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| Series: | EURASIP Journal on Image and Video Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13640-020-00528-0 |
| _version_ | 1818140391493337088 |
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| author | Herng-Hua Chang Po-Fang Chen Jun-Kai Guo Chia-Chi Sung |
| author_facet | Herng-Hua Chang Po-Fang Chen Jun-Kai Guo Chia-Chi Sung |
| author_sort | Herng-Hua Chang |
| collection | DOAJ |
| description | Abstract A high-quality underwater image is essential to many industrial and academic applications in the field of image processing and analysis. Unfortunately, underwater images frequently demonstrate poor visual quality of low contrast, blurring, darkness, and color diminishing. This paper develops a new underwater image restoration framework that consists of four major phases: color correction, local contrast enhancement, haze diminution, and global contrast enhancement. A self-adaptive mechanism is designed to guide the image to either processing route based on a red deficiency measure. In the color correction phase, the histogram in each RGB channel is transformed for balancing the image color. An adaptive histogram equalization method is exploited to enhance the local contrast in the CIE-Lab color space. The dark channel prior haze removal scheme is modified for dehazing in the haze diminution phase. Finally, a histogram stretching method is applied in the HSI color space to make the image more natural. A wide variety of underwater images with various scenarios were employed to evaluate this new restoration algorithm. Experimental results demonstrated the effectiveness of our image restoration scheme as compared with state-of-the-art methods. It was suggested that our framework dramatically eliminated the haze and improved visual interpretation of underwater images. |
| first_indexed | 2024-12-11T10:43:14Z |
| format | Article |
| id | doaj.art-084c3321f5cf425795de4b535a3eaa04 |
| institution | Directory Open Access Journal |
| issn | 1687-5281 |
| language | English |
| last_indexed | 2024-12-11T10:43:14Z |
| publishDate | 2020-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj.art-084c3321f5cf425795de4b535a3eaa042022-12-22T01:10:32ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812020-09-012020112110.1186/s13640-020-00528-0A self-adaptive single underwater image restoration algorithm for improving graphic qualityHerng-Hua Chang0Po-Fang Chen1Jun-Kai Guo2Chia-Chi Sung3Department of Engineering Science and Ocean Engineering, National Taiwan UniversityDepartment of Engineering Science and Ocean Engineering, National Taiwan UniversityDepartment of Engineering Science and Ocean Engineering, National Taiwan UniversityDepartment of Engineering Science and Ocean Engineering, National Taiwan UniversityAbstract A high-quality underwater image is essential to many industrial and academic applications in the field of image processing and analysis. Unfortunately, underwater images frequently demonstrate poor visual quality of low contrast, blurring, darkness, and color diminishing. This paper develops a new underwater image restoration framework that consists of four major phases: color correction, local contrast enhancement, haze diminution, and global contrast enhancement. A self-adaptive mechanism is designed to guide the image to either processing route based on a red deficiency measure. In the color correction phase, the histogram in each RGB channel is transformed for balancing the image color. An adaptive histogram equalization method is exploited to enhance the local contrast in the CIE-Lab color space. The dark channel prior haze removal scheme is modified for dehazing in the haze diminution phase. Finally, a histogram stretching method is applied in the HSI color space to make the image more natural. A wide variety of underwater images with various scenarios were employed to evaluate this new restoration algorithm. Experimental results demonstrated the effectiveness of our image restoration scheme as compared with state-of-the-art methods. It was suggested that our framework dramatically eliminated the haze and improved visual interpretation of underwater images.http://link.springer.com/article/10.1186/s13640-020-00528-0Underwater image restorationImage dehazingColor correctionHaze removalDark channel prior |
| spellingShingle | Herng-Hua Chang Po-Fang Chen Jun-Kai Guo Chia-Chi Sung A self-adaptive single underwater image restoration algorithm for improving graphic quality EURASIP Journal on Image and Video Processing Underwater image restoration Image dehazing Color correction Haze removal Dark channel prior |
| title | A self-adaptive single underwater image restoration algorithm for improving graphic quality |
| title_full | A self-adaptive single underwater image restoration algorithm for improving graphic quality |
| title_fullStr | A self-adaptive single underwater image restoration algorithm for improving graphic quality |
| title_full_unstemmed | A self-adaptive single underwater image restoration algorithm for improving graphic quality |
| title_short | A self-adaptive single underwater image restoration algorithm for improving graphic quality |
| title_sort | self adaptive single underwater image restoration algorithm for improving graphic quality |
| topic | Underwater image restoration Image dehazing Color correction Haze removal Dark channel prior |
| url | http://link.springer.com/article/10.1186/s13640-020-00528-0 |
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