A self-adaptive single underwater image restoration algorithm for improving graphic quality.
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| Title: | A self-adaptive single underwater image restoration algorithm for improving graphic quality. |
|---|---|
| Authors: | Chang, Herng-Hua1 (AUTHOR) herbertchang@ntu.edu.tw, Chen, Po-Fang1 (AUTHOR), Guo, Jun-Kai1 (AUTHOR), Sung, Chia-Chi1 (AUTHOR) |
| Source: | EURASIP Journal on Image & Video Processing. 9/14/2020, Vol. 2020 Issue 1, pN.PAG-N.PAG. 1p. |
| Subjects: | Image reconstruction, Image enhancement (Imaging systems), Algorithms, Image processing, Image analysis, Haze |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 145732818 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A self-adaptive single underwater image restoration algorithm for improving graphic quality. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chang%2C+Herng-Hua%22">Chang, Herng-Hua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> herbertchang@ntu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Po-Fang%22">Chen, Po-Fang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Jun-Kai%22">Guo, Jun-Kai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sung%2C+Chia-Chi%22">Sung, Chia-Chi</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22EURASIP+Journal+on+Image+%26+Video+Processing%22">EURASIP Journal on Image & Video Processing</searchLink>. 9/14/2020, Vol. 2020 Issue 1, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Haze%22">Haze</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s13640-020-00528-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Image reconstruction Type: general – SubjectFull: Image enhancement (Imaging systems) Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Image processing Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Haze Type: general Titles: – TitleFull: A self-adaptive single underwater image restoration algorithm for improving graphic quality. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chang, Herng-Hua – PersonEntity: Name: NameFull: Chen, Po-Fang – PersonEntity: Name: NameFull: Guo, Jun-Kai – PersonEntity: Name: NameFull: Sung, Chia-Chi IsPartOfRelationships: – BibEntity: Dates: – D: 14 M: 09 Text: 9/14/2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 16875176 Numbering: – Type: volume Value: 2020 – Type: issue Value: 1 Titles: – TitleFull: EURASIP Journal on Image & Video Processing Type: main |
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