天津科技大学学报2024，Vol.39Issue(3)：72-80,9.DOI:10.13364/j.issn.1672-6510.20230129
基于对比学习的深度残差网络图像超分辨率方法
Depth Residual Image Super-Resolution Based on Contrast Learning
陈亚瑞 1徐肖阳1
作者信息
- 1. 天津科技大学人工智能学院,天津 300457
- 折叠
摘要
Abstract
The traditional image super-resolution method based on contrast learning generally takes the original image as the positive sample and the degraded image or other types of images as the negative sample,which has the problem of poor tex-ture detail restoration.In this article,we propose a depth residual image super-resolution based on contrast learning(CEDSR)method.In our proposed method,for the residual super-resolution model,the sharpened images of high-resolution images are used as positive samples,and the slightly blurred images of high-resolution images are used as negative samples.The contrast loss lifting under positive and negative samples is used to restore and enhance the texture details.The positive sample images after enhancement and sharpening carry more abundant texture information,and the fuzzy negative sample images generated based on different functions depict the texture fuzzy features.The contrast loss of positive and negative samples is conducive to the restoration of texture details in super-resolution images.Our proposed model was compared with the classical algorithms on DIV2K,Set14,BSDS100 and Urban100 standard data sets.The qualitative and quantitative ex-perimental results show that the model has better super-resolution image.关键词
图像超分辨率/对比学习/残差网络Key words
image super-resolution/contrast learning/residual network分类
信息技术与安全科学引用本文复制引用
陈亚瑞,徐肖阳..基于对比学习的深度残差网络图像超分辨率方法[J].天津科技大学学报,2024,39(3):72-80,9.
