Segmentation-Aware Image Denoising without Knowing True Segmentation

[meton-robean]

截止2019-11-05 该论文是preprint Segmentation-Aware Image Denoising without Knowing True Segmentation

*相当与分割网络可以训练的参数只有最后转成K channels 的那些33卷积层 和上采样层，而且没有用到语义信息标签， 而且特征嵌入网络的参数是固定的，用的是IMAGENET的欲训练，，但是居然可以work!!!**

[meton-robean]

The critical difference between U-SAID and existing works lies in the high-level component of the cascade. Unlike [29], [12] that placed a pre-trained and fixed segmentation network with true segmentation labels given for training, we design a new unsupervised segmentation awareness (USA) module, that requires no segmentation labels to train with. 这篇作者认为他们没有像下面两个工作（无论是预训练分割网络还是联合训练分割网络），都接触利用语义网络标签信息

12 Zhiwen Fan, Liyan Sun, Xinghao Ding, Yue Huang, Congbo Cai, and John Paisley. A segmentation-aware deep fusion network for compressed sensing mri. arXiv preprint arXiv:1804.01210, 2018. 这篇是预训练的语义分割网络帮 医学图像处理任务

[20]Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, and Thomas S Huang. When image denoising meets high-level vision tasks: A deep learning approach. arXiv preprint arXiv:1706.04284, 2017. 这篇是去噪和语义分割网络联合训练