A novel image enhancement method based on image decomposition and deep neural networks
•We propose a novel image decomposition-based optimization model for low-light image enhancement, by using the total variation and multi-scale convolutional sparse coding to precisely represent illumination and reflectance layers.•We integrate deep unfolding networks into framework of the proposed o...
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| Veröffentlicht in: | Pattern recognition Jg. 172; S. 112371 |
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| Sprache: | Englisch |
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01.04.2026
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| ISSN: | 0031-3203 |
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| Abstract | •We propose a novel image decomposition-based optimization model for low-light image enhancement, by using the total variation and multi-scale convolutional sparse coding to precisely represent illumination and reflectance layers.•We integrate deep unfolding networks into framework of the proposed optimization algorithm.•The deep unfolding network automatically estimates the priors from training samples.•Experimental results demonstrate that the proposed method outperforms state-of-the-art image enhancement methods in terms of visual quality and robutsness.
Image decomposition and deep learning are active research areas in computer vision tasks, such as cartoon texture decomposition, low-light image enhancement, rain streak removal, image recovery, etc. This paper proposes a novel low-light image enhancement method by joining image decomposition and deep neural network techniques. We introduce a new image decomposition-based optimization model by incorporating the Tikhonov regularization and multi-scale convolutional sparse coding (MSCSC) to enhance image visual effects. To enhance robustness performance, we introduce a noise-free image decomposition error term to effectively suppress noise in low-light images. To effectively implement the proposed method, we incorporate a deep-unfolding neural network and an adaptive denoiser into the alternating direction method of multipliers (ADMM) framework. Since the deep unfolding network can effectively simulate the optimization algorithm process, the interpretability of the network model is increased. Moreover, through end-to-end training, we can automatically estimate the two priors and parameter settings from training samples. Finally, qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art image enhancement methods in terms of visual quality and robustness. The source code is available at https://github.com/cassiopeia-yxx/LLIE. |
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| AbstractList | •We propose a novel image decomposition-based optimization model for low-light image enhancement, by using the total variation and multi-scale convolutional sparse coding to precisely represent illumination and reflectance layers.•We integrate deep unfolding networks into framework of the proposed optimization algorithm.•The deep unfolding network automatically estimates the priors from training samples.•Experimental results demonstrate that the proposed method outperforms state-of-the-art image enhancement methods in terms of visual quality and robutsness.
Image decomposition and deep learning are active research areas in computer vision tasks, such as cartoon texture decomposition, low-light image enhancement, rain streak removal, image recovery, etc. This paper proposes a novel low-light image enhancement method by joining image decomposition and deep neural network techniques. We introduce a new image decomposition-based optimization model by incorporating the Tikhonov regularization and multi-scale convolutional sparse coding (MSCSC) to enhance image visual effects. To enhance robustness performance, we introduce a noise-free image decomposition error term to effectively suppress noise in low-light images. To effectively implement the proposed method, we incorporate a deep-unfolding neural network and an adaptive denoiser into the alternating direction method of multipliers (ADMM) framework. Since the deep unfolding network can effectively simulate the optimization algorithm process, the interpretability of the network model is increased. Moreover, through end-to-end training, we can automatically estimate the two priors and parameter settings from training samples. Finally, qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art image enhancement methods in terms of visual quality and robustness. The source code is available at https://github.com/cassiopeia-yxx/LLIE. |
| ArticleNumber | 112371 |
| Author | Xia, Youshen Xiao, Yao |
| Author_xml | – sequence: 1 givenname: Yao surname: Xiao fullname: Xiao, Yao email: xiaoyao227192@163.com organization: College of Artificial Intelligence, Anhui University, HeFei, China – sequence: 2 givenname: Youshen surname: Xia fullname: Xia, Youshen email: ysxia2001@163.com organization: College of Artificial Intelligence, Anhui University, HeFei, China |
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| Cites_doi | 10.1109/TCI.2024.3420942 10.1016/j.patcog.2024.111033 10.1109/TIP.2018.2839891 10.1016/j.knosys.2024.111779 10.1109/TCSVT.2022.3195996 10.1109/LSP.2022.3175096 10.1016/j.jvcir.2022.103712 10.1109/TIP.2021.3050850 10.1109/LSP.2012.2227726 10.1109/TCSVT.2021.3073371 10.1016/j.patcog.2022.109241 10.1016/j.patcog.2016.06.008 10.1109/TIP.2022.3189805 10.1016/j.patcog.2024.111076 10.1038/scientificamerican1277-108 10.1016/j.neucom.2022.12.043 10.1016/j.apm.2022.11.022 10.1109/TIP.2018.2810539 10.1109/TIP.2012.2214050 10.1109/TIP.2020.2974060 10.1073/pnas.80.16.5163 10.1109/TIP.2016.2639450 10.1109/TIP.2020.2984098 10.1023/A:1022314423998 10.1109/TIP.2006.888338 |
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| Keywords | Deep neural network Image decomposition Retinex theory Low-light image enhancement |
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| SubjectTerms | Deep neural network Image decomposition Low-light image enhancement Retinex theory |
| Title | A novel image enhancement method based on image decomposition and deep neural networks |
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