DL去燥之 DnCNNs:Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
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1、网络结构:
DnCNNs: feed-forward denoising convolutional neural networks
2、学习策略
Residual Learning:通过残差学习策略,在网络的隐层隐式地移除干净图片(clean image)。即输入为带噪的观测图片(noisy image),输出为移除了干净图片的残差(噪声)图片(residual image)。这样做的动机是,用残差学习刻画恒等映射或近似的恒等映射,效果要比直接学习干净图片要相对好。
Batch Normalization:进一步提高模型的稳定性和去燥效果。
3、适用范围
Gaussian Denoising:DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising);
Image Super-resolution;
JPEG Deblocking.
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