Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying invertible models to remove noise is challenging because the input is noisy, and the reversed output is clean, following two different distributions. We propose an invertible denoising network, InvDN, to address this challenge. InvDN transforms the noisy input into a low-resolution clean image and a latent representation containing noise. To discard noise and restore the clean image, InvDN replaces the noisy latent representation with another one sampled from a prior distribution during reversion. The denoising performance of InvDN is better than all the existing competitive models, achieving a new state-of-the-art result for the SIDD dataset while enjoying less run time. Moreover, the size of InvDN is far smaller, only having 4.2% of the number of parameters compared to the most recently proposed DANet. Further, via manipulating the noisy latent representation, InvDN is also able to generate noise more similar to the original one. Our code is available at: https://github.com/Yang-Liu1082/InvDN.git.

提出了一种基于可逆网络的去噪方法InvDN，通过将噪音输入转换成低分辨率的干净图像和包含噪音的潜在表示，再在反转时用来自先验分布的另一个采样的潜在表示来替换噪音表示，实现了对SIDD数据集最优的性能表现，且运行时间更短，模型大小只有最新DANet的4.2％。

可逆去噪网络：轻量级真实噪声去除方案