Abstract
Low-light image enhancement is still a challenging task nowadays. On one hand, sensitive methods tend to reproduce light images with severe noise and color deviation. On the other hand, insensitive methods can recover clear and natural results but with much lower brightness. Hence, this paper analyzes several basic mathematical models and then proposes a low-light image enhancement network (LLIENet) based on a basic mathematical model, which contains several modules. First, a MaskNet is proposed to estimate the global illumination prior. Second, BaseNet and CoefficientNet are used to decompose the low-light image into a lightened base image and a subtle coefficient map. Finally, a RefineNet is added to further refine high-frequency details and suppress noise and color deviation. Extensive experiments are evaluated to demonstrate the superiority of the proposed method over several state-of-the-art methods.
The first author is a student.
This work is supported by the grants of the National Natural Science Foundation of China (Nos. 61972129, 61877016), and the grant of the Key Research and Development Program in Anhui Province (No. 1804a09020036).
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Notes
- 1.
Our code is available at https://github.com/xiaonaa/LLIE-code.
- 2.
More results can be found in the supplementary file.
- 3.
More subjective comparisons can also be found in the supplementary file.
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Liu, X., Zhao, Y., Chen, Y., Jia, W., Wang, R., Liu, X. (2020). Estimated Exposure Guided Reconstruction Model for Low-Light Image Enhancement. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_16
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