ArticleSensors (Basel, Switzerland)2025
Enhancing Low-Light Images with Kolmogorov-Arnold Networks in Transformer Attention.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- KANWhisper: leveraging learnable activation functions for interpretable and efficient arabic automatic speech recognition.Scientific reports · 2026Article
- Low-light driver drowsiness detection for real-time safety assistance using dual attention mechanisms in deep learning model.Scientific reports · 2026Article
- Boosting Diffusion Networks with Deep External Context-Aware Encoders for Low-Light Image Enhancement.Sensors (Basel, Switzerland) · 2025Article
- DCD-Net: Decoupling-Centric Decomposition Network for Low-Light Image Enhancement.Sensors (Basel, Switzerland) · 2025Article
- AGSK-Net: Adaptive Geometry-Aware Stereo-KANformer Network for Global and Local Unsupervised Stereo Matching.Sensors (Basel, Switzerland) · 2025Article
- DLFE-Net: Preserving Details and Removing Noise Using HVI Color Space for Low-Light Image Enhancement.Sensors (Basel, Switzerland) · 2025Article
- DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
5 authors.
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Abstract
Low-light image enhancement (LLIE) techniques improve the performance of image sensors by enhancing visibility and details in poorly lit environments and have significantly benefited from recent research into Transformer models. This work presents a novel Transformer attention mechanism inspired by the Kolmogorov-Arnold representation theorem, incorporating learnable non-linearity and multivariate function decomposition. This innovative mechanism is the foundation of KAN-T, our proposed Transformer network. By enhancing feature flexibility and enabling the model to capture broader contextual information, KAN-T achieves superior performance. Our comprehensive experiments, both quantitative and qualitative, demonstrate that the proposed method achieves state-of-the-art performance in low-light image enhancement, highlighting its effectiveness and wide-ranging applicability. The code will be released upon publication.
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