去噪论文“CycleISP: Real Image Restoration via Improved Data Synthesis”
去噪是否是一个很容易得到数据集的方向呢？是，也许不是。 传统的去噪的方法在原图上添加高斯白噪声(Additive White Gaussian Noise, AWGN)，而不是真实世界中存在的噪声。在深度学习中，若是在这一类的数据集上训练的效果越好，它在真实世界的去噪的数据集上的效果就越离谱，好比论文A high-quality denoising dataset for smartphone c
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相关文章
- 1. 论文解读：Cycle ISP Real Image Restoration via Improved Data Synthesis
- 2. 图像去噪--Noise2Noise: Learning Image Restoration without Clean Data
- 3. [去噪论文]--ICCV2019-Real Image Denoising with Feature Attention
- 4. [论文]记 Improved Image Captioning via Policy Gradient optimization of SPIDEr 论文报告
- 5. 《Learning Enriched Features for Real Image Restoration and Enhancement》
- 6. Noise2Noise: Learning Image Restoration without Clean Data，CVPR2019
- 7. 论文阅读笔记之——《Image Restoration via Residual Non-local Attention Networks》
- 8. Generative Adversarial Text to Image Synthesis 论文解读
- 9. 论文笔记：Noise2Noise: Learning Image Restoration without Clean Data
- 10. 论文阅读——Towards Adversarial Retinal Image Synthesis