人工智能
计算机科学
图像复原
计算机视觉
压缩失真
特征提取
JPEG格式
残余物
变压器
图像质量
卷积神经网络
模式识别(心理学)
图像压缩
图像处理
图像(数学)
工程类
算法
电压
电气工程
作者
Jingyun Liang,Jiezhang Cao,Guolei Sun,Kai Zhang,Luc Van Gool,Radu Timofte
出处
期刊:International Conference on Computer Vision
日期:2021-10-01
被引量:1468
标识
DOI:10.1109/iccvw54120.2021.00210
摘要
Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer. SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection. We conduct experiments on three representative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks by up to 0.14∼0.45dB, while the total number of parameters can be reduced by up to 67%.
科研通智能强力驱动
Strongly Powered by AbleSci AI