Deep image denoising networks have achieved impressive success with the help of a considerably large number of synthetic train datasets. However, real-world denoising is a still challenging problem due to the dissimilarity between distributions of real and synthetic noisy datasets. Although several real-world noisy datasets have been presented, the number of train datasets (i.e., pairs of clean and real noisy images) is limited, and acquiring more real noise datasets is laborious and expensive. To mitigate this problem, numerous attempts to simulate real noise models using generative models have been studied. Nevertheless, previous works had to train multiple networks to handle multiple different noise distributions. By contrast, we propose a new generative model that can synthesize noisy images with multiple different noise distributions. Specifically, we adopt recent contrastive learning to learn distinguishable latent features of the noise. Moreover, our model can generate new noisy images by transferring the noise characteristics solely from a single reference noisy image. We demonstrate the accuracy and the effectiveness of our noise model for both known and unknown noise removal.

翻译：深度图像去噪网络借助大量合成训练数据集取得了显著成功。然而，由于真实噪声数据集与合成噪声数据集分布之间的差异性，真实场景去噪仍是一个具有挑战性的问题。尽管已有多个真实噪声数据集被提出，但训练数据集（即干净图像与真实噪声图像的配对）数量有限，且获取更多真实噪声数据集既费时又昂贵。为缓解这一问题，研究者尝试了多种利用生成模型模拟真实噪声模型的方法。然而，先前的工作需要训练多个网络来处理多种不同的噪声分布。相比之下，我们提出了一种新颖的生成模型，能够合成包含多种不同噪声分布的噪声图像。具体而言，我们采用最新的对比学习方法来学习噪声的可区分潜在特征。此外，我们的模型仅通过单张参考噪声图像即可迁移噪声特征，从而生成新的噪声图像。我们验证了所提噪声模型在已知与未知噪声去除中的准确性与有效性。