PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement

• URL: http://arxiv.org/abs/2505.09196v1
• Date: Wed, 14 May 2025 07:14:25 GMT
• Title: PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement
• Authors: Tong Li, Lizhi Wang, Hansen Feng, Lin Zhu, Hua Huang,
• Abstract summary: Low-light image enhancement (LLIE) is a fundamental task in computational photography, aiming to improve illumination, reduce noise, and enhance image quality.<n>We observe a phenomenon: resetting certain parameters to random values unexpectedly improves enhancement performance for some images.<n>The gene effect limits enhancement performance, as even random parameters can sometimes outperform learned ones, preventing models from fully utilizing their capacity.<n>Inspired by biological evolution, where adaptation to new environments relies on gene mutation and recombination, we propose parameter dynamic evolution (PDE) to adapt to different images and mitigate the gene effect.
• Score: 22.73061476533364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement (LLIE) is a fundamental task in computational photography, aiming to improve illumination, reduce noise, and enhance image quality. While recent advancements focus on designing increasingly complex neural network models, we observe a peculiar phenomenon: resetting certain parameters to random values unexpectedly improves enhancement performance for some images. Drawing inspiration from biological genes, we term this phenomenon the gene effect. The gene effect limits enhancement performance, as even random parameters can sometimes outperform learned ones, preventing models from fully utilizing their capacity. In this paper, we investigate the reason and propose a solution. Based on our observations, we attribute the gene effect to static parameters, analogous to how fixed genetic configurations become maladaptive when environments change. Inspired by biological evolution, where adaptation to new environments relies on gene mutation and recombination, we propose parameter dynamic evolution (PDE) to adapt to different images and mitigate the gene effect. PDE employs a parameter orthogonal generation technique and the corresponding generated parameters to simulate gene recombination and gene mutation, separately. Experiments validate the effectiveness of our techniques. The code will be released to the public.

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