Spatially-Adaptive Large-Kernel Network for Efficient Image Super-Resolution

Xuanhua He, Ke Cao, Tao Hu, Jie Zhang, Rui Li

Abstract: In the realm of image super-resolution (SR), efficiency on low-power devices remains a significant challenge due to the high computational demands of current methods. In response to this issue, we propose a novel Spatially-Adaptive Large-Kernel Network(SALKN) tailored for efficient image super-resolution. Inspired by the spectrum convolution theorem, we introduce a spatially-adaptive large kernel convolution unit integrated into a vision transformer architecture. This approach implements dynamic large kernel convolution through an input-adaptive frequency domain multiplication and multi-head mechanism, significantly reducing computational overhead. Our implementation approach, which involves dynamically generating spatially-adaptive frequency filters, enables the dynamic large kernel convolution to be performed with reduced computational costs, facilitating the realization of a global receptive field and promoting scale diversity within the features. Extensive experiments validate that SALKN outperforms existing efficient super-resolution methods with reduced complexity, achieving state-of-the-art performance.