In recent years, videos and images in 720p (HD), 1080p (FHD) and 4K (UHD) resolution have become more popular for display devices such as TVs, mobile phones and VR. However, these high resolution images cannot achieve the expected visual effect due to the limitation of the internet bandwidth, and bring a great challenge for super-resolution networks to achieve real-time performance. Following this challenge, we explore multiple efficient network designs, such as pixel-unshuffle, repeat upscaling, and local skip connection removal, and propose a fast and lightweight super-resolution network. Furthermore, by analyzing the applications of the idea of divide-and-conquer in super-resolution, we propose assembled convolutions which can adapt convolution kernels according to the input features. Experiments suggest that our method outperforms all the state-of-the-art efficient super-resolution models, and achieves optimal results in terms of runtime and quality. In addition, our method also wins the first place in NTIRE 2023 Real-Time Super-Resolution - Track 1 ($\times$2). The code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/AsConvSR

翻译：近年来，720p（高清）、1080p（全高清）和4K（超高清）分辨率的视频和图像在电视、手机和VR等显示设备中日益普及。然而，由于互联网带宽的限制，这些高分辨率图像难以达到预期的视觉效果，同时也为超分辨率网络实现实时性能带来了巨大挑战。针对这一挑战，我们探索了多种高效网络设计，如像素反洗牌、重复上采样和局部跳跃连接移除，并提出了一种快速轻量级的超分辨率网络。此外，通过分析分治思想在超分辨率中的应用，我们提出了组装卷积，其能够根据输入特征自适应调整卷积核。实验表明，我们的方法优于所有最先进的超分辨率高效模型，并在运行时间和质量方面取得了最优结果。此外，我们的方法还在NTIRE 2023实时超分辨率挑战赛赛道1（$\times$2）中获得第一名。代码将在https://gitee.com/mindspore/models/tree/master/research/cv/AsConvSR开源。