IMPROVING IMAGE VISIBILITY IN DARK CONDITIONS WITH DEEP LEARNING
Keywords:
Keywords: Low-light image enhancement, Image visibility improvement, Noise reduction, Histogram equalization,Contrast stretching, Traditional image processingAbstract
Low light circumstances make image capture and processing difficult, resulting in lower visibility andmore noise. Handcrafted image processing techniques like histogram equalization, contrast stretching,and noise reduction filters are used in low-light image enhancement. These approaches may improve,but they rarely achieve natural-looking outcomes
References
Guo, Chunle, et al. "Zero-reference deep curve estimation for low-light image enhancement." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.
Yang, Wenhan, et al. "From fidelity to perceptual quality: A semi-supervised approach for lowlight image enhancement." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.
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Published
2025-04-23
How to Cite
P. Vemulamma, P. Vijayalaxmi, Ambati Sanath Kumar, Gathpa Harikrishna Prasad, Podila Prashanth. (2025). IMPROVING IMAGE VISIBILITY IN DARK CONDITIONS WITH DEEP LEARNING . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 383–391. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2321
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