AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions
This addresses the problem of real-time super-resolution for display devices like TVs and phones, offering an incremental improvement in efficiency and quality.
The paper tackles the challenge of achieving real-time performance in super-resolution for high-resolution images under bandwidth constraints by proposing a fast and lightweight network with assembled convolutions, which outperforms state-of-the-art efficient models and wins first place in the NTIRE 2023 Real-Time Super-Resolution competition.
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