# 图像去噪RIDNet复现
## 项目资源对应文章
对应的论文精读:[【图像去噪】论文精读:Real Image Denoising with Feature Attention(RIDNet)-CSDN博客](https://blog.csdn.net/qq_36584673/article/details/141261836)
对应的论文复现:[【图像去噪】论文复现:适合新手小白的Pytorch版本RIDNet复现!-CSDN博客](https://blog.csdn.net/qq_36584673/article/details/141284977)
## 使用方法
同CBDNet的复现:[【图像去噪】论文复现:适合新手小白的Pytorch版本CBDNet复现!轻松跑通训练和测试代码!简单修改路径即可训练自己的数据集!代码详细注释!数据处理、模型训练和验证、推理测试全流程讲解!-CSDN博客](https://blog.csdn.net/qq_36584673/article/details/141191222)
## 相关内容
高质量图像去噪算法专栏:[深度学习图像去噪算法100例_十小大的博客-CSDN博客](https://blog.csdn.net/qq_36584673/category_12756147.html)
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温馨提示
资源配套文章:https://blog.csdn.net/qq_36584673/article/details/141284977 项目文件说明: data:测试单张图像文件夹 datasets:数据集所在文件夹 weights:训练模型保存位置 loader.py:封装数据集 predict.py:测试单张图像去噪视觉效果 RIDNet.py:RIDNet模型实现 test_benchmark.py:计算测试集PSNR/SSIM,保存测试集图像去噪结果 test_noise.py:测试图像加噪效果 train.py:训练RIDNet utils.py:工具类脚本,包含一些图像操作 使用方式:见下面的readme.md
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