End-to-End Unpaired Image Denoising with Conditional Adversarial Networks


  • Zhiwei Hong Tsinghua University
  • Xiaocheng Fan Haohua Technology Co., Ltd
  • Tao Jiang University of California, Riverside
  • Jianxing Feng Haohua Technology Co., Ltd




Image denoising is a classic low level vision problem that attempts to recover a noise-free image from a noisy observation. Recent advances in deep neural networks have outperformed traditional prior based methods for image denoising. However, the existing methods either require paired noisy and clean images for training or impose certain assumptions on the noise distribution and data types. In this paper, we present an end-to-end unpaired image denoising framework (UIDNet) that denoises images with only unpaired clean and noisy training images. The critical component of our model is a noise learning module based on a conditional Generative Adversarial Network (cGAN). The model learns the noise distribution from the input noisy images and uses it to transform the input clean images to noisy ones without any assumption on the noise distribution and data types. This process results in pairs of clean and pseudo-noisy images. Such pairs are then used to train another denoising network similar to the existing denoising methods based on paired images. The noise learning and denoising components are integrated together so that they can be trained end-to-end. Extensive experimental evaluation has been performed on both synthetic and real data including real photographs and computer tomography (CT) images. The results demonstrate that our model outperforms the previous models trained on unpaired images as well as the state-of-the-art methods based on paired training data when proper training pairs are unavailable.




How to Cite

Hong, Z., Fan, X., Jiang, T., & Feng, J. (2020). End-to-End Unpaired Image Denoising with Conditional Adversarial Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4140-4149. https://doi.org/10.1609/aaai.v34i04.5834



AAAI Technical Track: Machine Learning