ZS-KAN: Zero-shot Image Denoising with Lightweight Kolmogorov-Arnold Networks

Current learning-based image denoising methods have achieved impressive performance. However, their reliance on deep neural architectures and large paired datasets limits their applicability in data-limited or edge computing scenarios. Motivated by the expressive functional approximation power of Kolmogorov-Arnold networks (KANs), here we present ZS-KAN-a lightweight yet highly effective and computationally efficient zero-shot denoising method. ZS-KAN combines the computational efficiency of convolutional neural networks with the representational flexibility of KANs, achieving competitive denoising performance while requiring only 1%-25% of the parameters used by other recent zero-shot approaches. Experimental results on synthetic and real-world noisy data demonstrate that ZS-KAN achieves comparable or even superior performance to state-of-the-art zero-shot methods while maintaining significantly lower model complexity. These advantages highlight the potential of ZS-KAN for practical deployment. The PyTorch implementation is publicly available at: https://github.com/Jayx-Wang/ZS-KAN.