KAN See In the Dark
Aoxiang Ning, Minglong Xue, Jinhong He, Chengyun Song

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Enhancement Techniques · Generative Adversarial Networks and Image Synthesis · Advanced Image Processing Techniques
MethodsDiffusion
