Abstract
Former unsupervised extremely low-light image enhancement methods suffer from two issues: semantic information loss and insufficient noise suppression. To overcome these two problems, we propose an unsupervised Extremely Low-light image enhancement via a Laplacian Pyramid Network (ELLPN). Concretely, concerning the first quandary, we propose to enforce semantic content and style constraints in the low-frequency components of the image’s Laplacian pyramid after histogram equalization, therefore realizing image enhancement. As for the second issue, a generalized denoising module is introduced to process the high-frequency components of the image’s Laplacian pyramid after histogram equalization, thus further restoring the details of the image. Extensive analytical experiments substantiate the efficacy of our approach.
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Acknowledgement
This work is partly supported by National Science Foundation, China (No: 62201213) and an Open Project of the Key Laboratory of System Control and Information Processing, Ministry of Education (Shanghai JiaoTong University, ID: Scip20230105).
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Ma, Y., Xie, S., Xu, W. (2024). Unsupervised Extremely Low-Light Image Enhancement with a Laplacian Pyramid Network. In: Huang, DS., Chen, W., Pan, Y. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14869. Springer, Singapore. https://doi.org/10.1007/978-981-97-5603-2_10
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DOI: https://doi.org/10.1007/978-981-97-5603-2_10
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