论文标题
分配条件denoising：灵活的判别图像Denoiser
Distribution Conditional Denoising: A Flexible Discriminative Image Denoiser
论文作者
论文摘要
引入了灵活的判别图像Denoiser，其中将多任务学习方法应用于基于U-NET的致密FCN。 U-NET模型的激活是通过仿射变换来修改的，仿射变换是调理输入的学习函数。多种噪声类型和级别的学习过程涉及在训练期间应用噪声参数的分布到调节输入中，并且在输入处将相同的噪声参数应用于噪声生成层（类似于DeNoising AutoConcododer中采用的方法）。结果表明，这种灵活的DeNoising模型在被高斯和泊松噪声破坏的图像上实现了最先进的性能。还已经表明，这种条件训练方法可以将固定噪声水平的u-net deoiser推广到各种噪声水平。
A flexible discriminative image denoiser is introduced in which multi-task learning methods are applied to a densoising FCN based on U-Net. The activations of the U-Net model are modified by affine transforms that are a learned function of conditioning inputs. The learning procedure for multiple noise types and levels involves applying a distribution of noise parameters during training to the conditioning inputs, with the same noise parameters applied to a noise generating layer at the input (similar to the approach taken in a denoising autoencoder). It is shown that this flexible denoising model achieves state of the art performance on images corrupted with Gaussian and Poisson noise. It has also been shown that this conditional training method can generalise a fixed noise level U-Net denoiser to a variety of noise levels.
