Research Paper#Image Super-Resolution, Deep Learning, Kolmogorov-Arnold Theorem🔬 ResearchAnalyzed: Jan 3, 2026 19:33
KANO: Interpretable Super-Resolution with Kolmogorov-Arnold Theorem
Published:Dec 28, 2025 07:27
•1 min read
•ArXiv
Analysis
This paper introduces KANO, a novel interpretable operator for single-image super-resolution (SR) based on the Kolmogorov-Arnold theorem. It addresses the limitations of existing black-box deep learning approaches by providing a transparent and structured representation of the image degradation process. The use of B-spline functions to approximate spectral curves allows for capturing key spectral characteristics and endowing SR results with physical interpretability. The comparative study between MLPs and KANs offers valuable insights into handling complex degradation mechanisms.
Key Takeaways
- •Proposes KANO, a novel interpretable operator for image super-resolution.
- •KANO is based on the Kolmogorov-Arnold theorem.
- •Uses B-spline functions for spectral curve approximation.
- •Offers physical interpretability to SR results.
- •Provides a comparative study of MLPs and KANs.
Reference
“KANO provides a transparent and structured representation of the latent degradation fitting process.”