Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement
Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility. Since the noise exhibits different levels, its estimation has been implemented in recent works when enhancing low-light images from raw Bayer space. When it comes to sRGB color space, the noi...
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| Vydané v: | Proceedings / IEEE International Conference on Computer Vision s. 4419 - 4428 |
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01.10.2021
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| Abstract | Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility. Since the noise exhibits different levels, its estimation has been implemented in recent works when enhancing low-light images from raw Bayer space. When it comes to sRGB color space, the noise estimation becomes more complicated due to the effect of the image processing pipeline. Nevertheless, most existing enhancing algorithms in sRGB space only focus on the low visibility problem or suppress the noise under a hypothetical noise level, leading them impractical due to the lack of robustness. To address this issue, we propose an adaptive unfolding total variation network (UTVNet), which approximates the noise level from the real sRGB low-light image by learning the balancing parameter in the model-based denoising method with total variation regularization. Meanwhile, we learn the noise level map by unrolling the corresponding minimization process for providing the inferences of smoothness and fidelity constraints. Guided by the noise level map, our UTVNet can recover finer details and is more capable to suppress noise in real captured low-light scenes. Extensive experiments on real-world low-light images clearly demonstrate the superior performance of UTVNet over state-of-the-art methods. |
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| AbstractList | Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility. Since the noise exhibits different levels, its estimation has been implemented in recent works when enhancing low-light images from raw Bayer space. When it comes to sRGB color space, the noise estimation becomes more complicated due to the effect of the image processing pipeline. Nevertheless, most existing enhancing algorithms in sRGB space only focus on the low visibility problem or suppress the noise under a hypothetical noise level, leading them impractical due to the lack of robustness. To address this issue, we propose an adaptive unfolding total variation network (UTVNet), which approximates the noise level from the real sRGB low-light image by learning the balancing parameter in the model-based denoising method with total variation regularization. Meanwhile, we learn the noise level map by unrolling the corresponding minimization process for providing the inferences of smoothness and fidelity constraints. Guided by the noise level map, our UTVNet can recover finer details and is more capable to suppress noise in real captured low-light scenes. Extensive experiments on real-world low-light images clearly demonstrate the superior performance of UTVNet over state-of-the-art methods. |
| Author | Shi, Daming Zheng, Chuanjun Shi, Wentian |
| Author_xml | – sequence: 1 givenname: Chuanjun surname: Zheng fullname: Zheng, Chuanjun email: zhengchuanjun2019@email.szu.edu.cn organization: Shenzhen University – sequence: 2 givenname: Daming surname: Shi fullname: Shi, Daming email: dshi@szu.edu.cn organization: Shenzhen University – sequence: 3 givenname: Wentian surname: Shi fullname: Shi, Wentian email: shiwentian2018@email.szu.edu.cn organization: Shenzhen University |
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| Snippet | Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility. Since the noise exhibits different levels, its... |
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| SubjectTerms | Adaptation models Adaptive systems Estimation Low-level and physics-based vision Minimization Noise reduction Pipelines |
| Title | Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement |
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