  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
