September 16, 2025

Review of noise reduction algorithms for low illumination images

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This review critically evaluates noise reduction algorithms for low illumination images, highlighting key methods.
It categorizes leading algorithms into filter-based and deep learning approaches, providing comprehensive insights.
The analysis includes theoretical frameworks and experimental evaluations, showcasing strengths and weaknesses of various techniques.
Future directions emphasize developing lightweight models and exploring self-supervised methodologies in image processing.

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This review categorizes noise reduction algorithms into filter-based and deep learning methods, suggesting future enhancements.

Dong et al. (2025) studied this question.

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