PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement
Journal:
arXiv
Published Date:
May 14, 2025
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.
