KAN See In the Dark

TL;DR

This paper introduces a novel low-light image enhancement method using Kolmogorov-Arnold networks (KANs) with a KAN-Block, incorporating frequency-domain perception to improve visual quality and address nonlinear relationships in low-light conditions.

Contribution

The paper proposes a new KAN-Block based on KANs for low-light enhancement, integrating frequency-domain perception to overcome limitations of existing linear and non-interpretable models.

Findings

Achieves competitive results on benchmark datasets

Effectively captures nonlinear dependencies in low-light images

Enhances visual quality through frequency-domain perception

Abstract

Existing low-light image enhancement methods are difficult to fit the complex nonlinear relationship between normal and low-light images due to uneven illumination and noise effects. The recently proposed Kolmogorov-Arnold networks (KANs) feature spline-based convolutional layers and learnable activation functions, which can effectively capture nonlinear dependencies. In this paper, we design a KAN-Block based on KANs and innovatively apply it to low-light image enhancement. This method effectively alleviates the limitations of current methods constrained by linear network structures and lack of interpretability, further demonstrating the potential of KANs in low-level vision tasks. Given the poor perception of current low-light image enhancement methods and the stochastic nature of the inverse diffusion process, we further introduce frequency-domain perception for visually oriented enhancement. Extensive experiments demonstrate the competitive performance of our method on benchmark datasets. The code will be available at: https://github.com/AXNing/KSID}{https://github.com/AXNing/KSID.