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

TL;DR

AsConvSR introduces a fast, lightweight super-resolution network utilizing assembled convolutions and efficient design strategies, achieving state-of-the-art real-time performance and quality for high-resolution images.

Contribution

The paper proposes assembled convolutions and optimized network design for real-time super-resolution, outperforming existing models in speed and quality.

Findings

Outperforms state-of-the-art super-resolution models

Achieves real-time processing with high quality

Wins first place in NTIRE 2023 challenge

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