Related papers: AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions

AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions

URL: http://arxiv.org/abs/2305.03387v1
Date: Fri, 5 May 2023 09:33:34 GMT
Title: AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions
Authors: Jiaming Guo, Xueyi Zou, Yuyi Chen, Yi Liu, Jia Hao, Jianzhuang Liu, Youliang Yan
Abstract summary: We propose a fast and lightweight super-resolution network to achieve real-time performance. By analyzing the applications of divide-and-conquer in super-resolution, we propose assembled convolutions which can adapt convolution kernels according to the input features. Our method also wins the first place in NTIRE 2023 Real-Time Super-Resolution - Track 1.
Score: 32.85522513271578
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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