Fully $1\times1$ Convolutional Network for Lightweight Image Super-Resolution

Abstract--Deep models have achieved significant process on single image super-resolution (SISR) tasks, in particular large models with large kernel (3 3 or more). However, the heavy computational footprint of such models prevents their deployment in real-time, resource-constrained environments. Conversely, 1 1 convolutions bring substantial computational efficiency, but struggle with aggregating local spatial representations, an essential capability to SISR models. In response to this dichotomy, we propose to harmonize the merits of both 3 3 and 1 1 kernels, and exploit a great potential for lightweight SISR tasks. Specifically, we propose a simple yet effective fully 1 1 convolutional network, named Shift-Conv-based Network (SCNet). By incorporating a parameter-free spatial-shift operation, it equips the fully 1 1 convolutional network with powerful representation capability while impressive computational efficiency. The 3 3 convolution operation is the most widely used Single image super-resolution (SISR) aims at reconstructing operation in CNN-based models due to its advantageous in a high-resolution (HR) image from its corresponding balancing the model capacity and computational cost.