Abstract
Improving low-light images is vital for fields like surveillance and medical imaging, where sharp and clear visuals are critical for accurate analysis. The primary challenge is the limited availability of high-quality data and the difficulty in enhancing image details without introducing errors. To address this challenge, we propose improving low-light enhancement through day-to-night domain adaptation. This approach aims to aid low-light scene enhancement by recovering well-lit scenes after degradation, eliminating the need for real low-light data. High-Quality Priors (HQPs) are introduced through discrete codebooks using a pre-trained VQGAN, Retinex-based reflectance decomposition, FFT-based feature transformation, and a bionic signal amplification mechanism. A comprehensive loss function ensures high-quality enhancement. Experimental results demonstrate that this method excels in noise reduction, detail preservation, and overall visual quality.
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Acknowledgment
This work was supported by the innovation fund (No.2021YFB3601400). I am deeply grateful to the development team of the SEE series software for providing the essential simulation support that greatly contributed to the completion of this research.
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Li, Z., Yang, H., Yang, S., Tang, X., Xu, F., Chen, Q. (2025). HQPAFT: Enhancing Low-Light Images with High-Quality Priors and Advanced Feature Transformations Using Only Normal Light Images. In: Hadfi, R., Anthony, P., Sharma, A., Ito, T., Bai, Q. (eds) PRICAI 2024: Trends in Artificial Intelligence. PRICAI 2024. Lecture Notes in Computer Science(), vol 15283. Springer, Singapore. https://doi.org/10.1007/978-981-96-0122-6_19
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