SRGAN-CKAN: Expressive Super-Resolution with Nonlinear Functional Operators under Minimal Resources

TL;DR

This paper introduces SRGAN-CKAN, a resource-efficient super-resolution framework that enhances local operator expressivity using nonlinear functional transformations, balancing perceptual quality and fidelity.

Contribution

It proposes a novel hybrid super-resolution method integrating CKAN with nonlinear patch-based operators, improving local detail modeling under minimal computational resources.

Findings

Improved perceptual quality with preserved reconstruction fidelity.

Achieved a favorable balance between distortion and perceptual metrics.

Operates efficiently under constrained computational settings.

Abstract

Single-Image Super-Resolution (SISR) aims to reconstruct a High-Resolution (HR) image from a Low-Resolution (LR) observation, a fundamentally ill-posed problem where high-frequency details are severely degraded at large upscaling factors. Recent advances have been driven by transformer-based architectures and diffusion models improve global context modeling and perceptual quality at the cost of increased computational complexity. In contrast, this work focuses on enhancing the expressivity of local operators under minimal resources. We propose SRGAN--CKAN, a hybrid super-resolution framework that integrates Convolutional Kolmogorov--Arnold Networks (CKAN) into an adversarial learning setting reformulating convolution as a nonlinear patch-based transformation. The proposed operator replaces linear local mappings with spline-based functional representations, allowing expressive modeling of complex local structures and high-frequency textures using minimal hardware resources. Experimental results demonstrate that the proposed approach improves perceptual quality while preserving reconstruction fidelity, achieving a favorable balance between distortion-based and perceptual metrics. These results are obtained under constrained computational settings, highlighting the efficiency of the proposed formulation. Overall, this work introduces a complementary direction to existing approaches by improving the representational power of local transformations, providing an efficient and scalable alternative to globally intensive architectures.