Daokuan Qu ^1* Yuyao Ke ²

• ¹ School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
• ² School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

Recently, significant advancements have been made in the field of efficient single-image superresolution, primarily driven by the innovative concept of information distillation. This method adeptly leverages multi-level features to facilitate high-resolution image reconstruction, allowing for enhanced detail and clarity. However, many existing approaches predominantly emphasize the enhancement of distilled features, often overlooking the critical aspect of improving the feature extraction capabilities of the distillation module itself. In this paper, we address this limitation by introducing an asymmetric large-kernel convolution design. By increasing the size of the convolution kernel, we expand the receptive field, which enables the model to more effectively capture long-range dependencies among image pixels. This enhancement significantly improves the model's perceptual ability, leading to more accurate reconstructions.To maintain a manageable level of model complexity, we adopt a lightweight architecture that employs asymmetric convolution techniques. Building on this foundation, we propose the Lightweight Asymmetric Large Kernel Distillation Network (ALKDNet). Comprehensive experiments reveal that ALKDNet not only preserves efficiency but also achieves state-of-the-art performance compared to existing super-resolution methods. The source code is available at https://github.com/Qudaokuan/ALKDNet.