Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
Addressing the common issues of low brightness, poor contrast, and blurred details in images captured under conditions such as night, backlight, and adverse weather, we propose a zero-reference dual-path network based on multi-scale depth curve estimation for low-light image enhancement. Utilizing a...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/701 |
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| author | Chengkang Yu Guangliang Han Mengyang Pan Xiaotian Wu Anping Deng |
| author_facet | Chengkang Yu Guangliang Han Mengyang Pan Xiaotian Wu Anping Deng |
| author_sort | Chengkang Yu |
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| description | Addressing the common issues of low brightness, poor contrast, and blurred details in images captured under conditions such as night, backlight, and adverse weather, we propose a zero-reference dual-path network based on multi-scale depth curve estimation for low-light image enhancement. Utilizing a no-reference loss function, the enhancement of low-light images is converted into depth curve estimation, with three curves fitted to enhance the dark details of the image: a brightness adjustment curve (LE-curve), a contrast enhancement curve (CE-curve), and a multi-scale feature fusion curve (MF-curve). Initially, we introduce the TCE-L and TCE-C modules to improve image brightness and enhance image contrast, respectively. Subsequently, we design a multi-scale feature fusion (MFF) module that integrates the original and enhanced images at multiple scales in the HSV color space based on the brightness distribution characteristics of low-light images, yielding an optimally enhanced image that avoids overexposure and color distortion. We compare our proposed method against ten other advanced algorithms based on multiple datasets, including LOL, DICM, MEF, NPE, and ExDark, that encompass complex illumination variations. Experimental results demonstrate that the proposed algorithm adapts better to the characteristics of images captured in low-light environments, producing enhanced images with sharp contrast, rich details, and preserved color authenticity, while effectively mitigating the issue of overexposure. |
| format | Article |
| id | doaj-art-6acf20a2df354121b19ecbb7b4862a7c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6acf20a2df354121b19ecbb7b4862a7c2025-01-24T13:20:31ZengMDPI AGApplied Sciences2076-34172025-01-0115270110.3390/app15020701Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light ImagesChengkang Yu0Guangliang Han1Mengyang Pan2Xiaotian Wu3Anping Deng4Changchun 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 Physics, Northeast Normal University, Changchun 130024, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaAddressing the common issues of low brightness, poor contrast, and blurred details in images captured under conditions such as night, backlight, and adverse weather, we propose a zero-reference dual-path network based on multi-scale depth curve estimation for low-light image enhancement. Utilizing a no-reference loss function, the enhancement of low-light images is converted into depth curve estimation, with three curves fitted to enhance the dark details of the image: a brightness adjustment curve (LE-curve), a contrast enhancement curve (CE-curve), and a multi-scale feature fusion curve (MF-curve). Initially, we introduce the TCE-L and TCE-C modules to improve image brightness and enhance image contrast, respectively. Subsequently, we design a multi-scale feature fusion (MFF) module that integrates the original and enhanced images at multiple scales in the HSV color space based on the brightness distribution characteristics of low-light images, yielding an optimally enhanced image that avoids overexposure and color distortion. We compare our proposed method against ten other advanced algorithms based on multiple datasets, including LOL, DICM, MEF, NPE, and ExDark, that encompass complex illumination variations. Experimental results demonstrate that the proposed algorithm adapts better to the characteristics of images captured in low-light environments, producing enhanced images with sharp contrast, rich details, and preserved color authenticity, while effectively mitigating the issue of overexposure.https://www.mdpi.com/2076-3417/15/2/701low-light image enhancementzero-referencecurve toningdeep learning |
| spellingShingle | Chengkang Yu Guangliang Han Mengyang Pan Xiaotian Wu Anping Deng Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images Applied Sciences low-light image enhancement zero-reference curve toning deep learning |
| title | Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images |
| title_full | Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images |
| title_fullStr | Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images |
| title_full_unstemmed | Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images |
| title_short | Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images |
| title_sort | zero tce zero reference tri curve enhancement for low light images |
| topic | low-light image enhancement zero-reference curve toning deep learning |
| url | https://www.mdpi.com/2076-3417/15/2/701 |
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