1D kernel distillation network for efficient image super-resolution

Abstract

Recently, there have been significant strides in single-image super-resolution, especially with the integration of transformers. However, the escalating computational demands of large models pose challenges for deployment on edge devices. Therefore, in pursuit of Efficient Image Super-Resolution (EISR), achieving a better balance between task computational complexity and image fidelity becomes imperative. In this paper, we introduce the 1D kernel distillation network (OKDN). Within this network, we have devised a lightweight 1D Large Kernel (OLK) block, incorporating a more lightweight yet highly effective attention mechanism. This block significantly expands the effective receptive field, enhancing performance while mitigating computational costs. Additionally, we develop a Channel Shift Enhanced Distillation (CSED) block to improve distillation efficiency, allocating more computational resources towards increasing network depth. We utilize methods involving partial channel shifting and global feature supervision (GFS) to further augment the effective receptive field. Furthermore, we introduce learnable Gaussian perturbation convolution (LGPConv) to enhance the model’s feature extraction and performance capabilities while upholding low computational complexity. Experimental results demonstrate that our proposed approach achieves superior results with significantly lower computational complexity compared to state-of-the-art models. The code is available at https://github.com/satvio/OKDN.