Deep models have achieved significant process on single image super-resolution (SISR) tasks, in particular large models with large kernel ($3\times3$ or more). However, the heavy computational footprint of such models prevents their deployment in real-time, resource-constrained environments. Conversely, $1\times1$ 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 $3\times3$ and $1\times1$ kernels, and exploit a great potential for lightweight SISR tasks. Specifically, we propose a simple yet effective fully $1\times1$ convolutional network, named Shift-Conv-based Network (SCNet). By incorporating a parameter-free spatial-shift operation, it equips the fully $1\times1$ convolutional network with powerful representation capability while impressive computational efficiency. Extensive experiments demonstrate that SCNets, despite its fully $1\times1$ convolutional structure, consistently matches or even surpasses the performance of existing lightweight SR models that employ regular convolutions.

基于参数无关的空间位移操作，我们提出了一种简单而有效的全 $1	imes1$ 卷积网络，名为 Shift-Conv-based Network (SCNet)，在保持令人印象深刻的计算效率的同时，赋予全 $1	imes1$ 卷积网络强大的表示能力。大量实验证明，尽管结构为全 $1	imes1$ 卷积，SCNet 一致性地达到或超过使用常规卷积的现有轻量级超分辨率模型的性能。

轻量级图像超分辨率的全一次卷积网络