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.
Abstract (translated)
低光照图像增强（LLIE）是计算摄影中的一个基本任务，旨在提高照明、减少噪声并提升图像质量。尽管最近的研究重点在于设计越来越复杂的神经网络模型，但我们观察到一种奇特的现象：将某些参数重置为随机值有时会意外地改善部分图像的增强效果。受到生物学基因概念的启发，我们将这一现象称为“基因效应”。基因效应限制了增强性能，因为即使随机参数也有可能优于学习得到的参数，阻碍模型充分发挥其潜力。 在本文中，我们探讨了产生这种现象的原因，并提出了解决方案。根据我们的观察，我们认为基因效应是由静态参数引起的，就像固定不变的遗传配置会在环境变化时变得适应性差一样。受生物进化启发，即适应新环境需要通过基因突变和重组来实现，我们提出了动态参数演化（PDE）方法以适应不同的图像并缓解基因效应。 PDE采用了一种参数正交生成技术以及对应生成的参数，分别模拟了基因重组与基因突变的过程。实验验证了我们的技术的有效性。代码将公开发布。
URL
https://arxiv.org/abs/2505.09196
