Noise2Noise: Learning Image Restoration without Clean Data，CVPR2019
本篇文章是CVPR19年的文章，研究任务是去噪，motivation在于提升模型应用性，做到既不需要干净图像，也不像Noise2Noise那样需要噪声图像对，直接在噪声图像上训练。应用性可以，去噪效果上是不如N2N或完全有监督模型的。 假设 Method部分，在x=s+n即图像=信号+噪声的基础上，作者做出了几个重要假设： （1） 干净图像的临近pixel是相关的，不独立，比较reasonable
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相关文章
- 1. 论文笔记：Noise2Noise: Learning Image Restoration without Clean Data
- 2. 图像去噪--Noise2Noise: Learning Image Restoration without Clean Data
- 3. Discriminative Transfer Learning for General Image Restoration
- 4. Image Restoration-Image Diffusion
- 5. 《Learning Enriched Features for Real Image Restoration and Enhancement》
- 6. Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration（cvpr2019）
- 7. Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning摘要翻译
- 8. Noise2Noise文章的复现code实现
- 9. 论文解读：Cycle ISP Real Image Restoration via Improved Data Synthesis
- 10. Multi Institution Deep Learning Modeling Without Sharing Patient Data