# SR **Repository Path**: CHH12/sr ## Basic Information - **Project Name**: SR - **Description**: Software B - **Primary Language**: Python - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-04-02 - **Last Updated**: 2021-08-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 自评测的图像超分处理系统 #### 介绍 本项目系HITSZ-2021春《软件设计与开发实践B》课程项目 小组成员:陈颢、张英东、叶茂盛、蒋深远 #### 软件架构 mainWindow.py为本系统主文件,包含主要的控制逻辑; UI.py为本系统基于PyQt5的可视化界面代码,由UI.ui经过pyuic自动生成。 ./codes下为超分辨率处理的代码 #### 项目主要依赖 Python == 3.7.0(其他版本未测试) pytorch >= 1.5.0(其他版本未测试) #### 使用说明 1. 在Pycharm中打开本项目,配置解释器 2. 在mainWindow.py文件下点击运行即可 #### 系统截图 ![初始界面](https://images.gitee.com/uploads/images/2021/0515/011606_65285773_8065980.jpeg "1.jpg") ![超分输出](https://images.gitee.com/uploads/images/2021/0515/011625_d671feb3_8065980.jpeg "2.jpg") ![超分评测](https://images.gitee.com/uploads/images/2021/0515/011637_c2176864_8065980.jpeg "3.jpg") #### 参考文献 [1] Kong X , Zhao H , Y Qiao, et al. ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic[J]. 2021. [2] Umer R M , Micheloni C . Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution[J]. 2020. [3] Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. [4] Chao D , Chen C L , Tang X . Accelerating the Super-Resolution Convolutional Neural Network[J]. Springer, Cham, 2016. [5] Zhang Y , Li K , Li K , et al. Image Super-Resolution Using Very Deep Residual Channel Attention Networks[C]// 2018. [6] Chen H , He X , Qing L , et al. Real-World Single Image Super-Resolution: A Brief Review[J]. 2021. [7] Dong C , Loy C C , He K , et al. Image Super-Resolution Using Deep Convolutional Networks[J]. IEEE Trans Pattern Anal Mach Intell, 2016, 38(2):295-307. #### 参考链接 https://github.com/ChaofWang/Awesome-Super-Resolution