Unprocessing Images for Learned Raw Denoising
Abstract 当用于训练的数据与用于评估的数据类似时，机器学习技术最有效。这对于学习过的单图像去噪算法来讲是正确的，这些算法应用于真实的原始相机传感器读数，但因为实际的限制，一般在合成图像数据上进行训练。虽然从合成图像推广到真实图像须要仔细考虑相机传感器的噪声特性，图像处理管道的其余方面(如增益、颜色校订和色调映射)经常被忽略，尽管它们对原始测量数据如何转换成最终图像有重要影响。为了解决这个问
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相关文章
- 1. 读论文：Unprocessing Images for Learned Raw Denoising
- 2. 论文解读：Unprocessing Images for Learned Raw Denoising
- 3. 《Unprocessing Images for Learned Raw Denoising》论文阅读
- 4. Fast Burst Images Denoising
- 5. Noise2Void - Learning Denoising from Single Noisy Images
- 6. 论文笔记：Learning Denoising from Single Noisy Images
- 7. 论文阅读《A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising》CVPR2020
- 8. A LEARNED REPRESENTATION FOR ARTISTIC STYLE
- 9. Deeply Learned Attributes for Crowded Scene Understanding（WWW dataset）
- 10. Paper | Dynamic Residual Dense Network for Image Denoising