With recent deep learning based approaches showing promising results in removing noise from images, the best denoising performance has been reported in a supervised learning setup that requires a large set of paired noisy images and ground truth for training. The strong data requirement can be mitigated by unsupervised learning techniques, however, accurate modelling of images or noise variance is still crucial for high-quality solutions. The learning problem is ill-posed for unknown noise distributions. This paper investigates the tasks of image denoising and noise variance estimation in a single, joint learning framework. To address the ill-posedness of the problem, we present deep variation prior (DVP), which states that the variation of a properly learnt denoiser with respect to the change of noise satisfies some smoothness properties, as a key criterion for good denoisers. Building upon DVP, an unsupervised deep learning framework, that simultaneously learns a denoiser and estimates noise variances, is developed. Our method does not require any clean training images or an external step of noise estimation, and instead, approximates the minimum mean squared error denoisers using only a set of noisy images. With the two underlying tasks being considered in a single framework, we allow them to be optimised for each other. The experimental results show a denoising quality comparable to that of supervised learning and accurate noise variance estimates.

最近基于深度学习的方法在以下方面显示出良好的结果 去除图像中的噪声，已报道了最好的去噪性能 需要一大组配对噪声图像的有监督学习设置 和训练用的地面真相。可以通过以下方式缓解强烈的数据需求 然而，无监督的学习技术，图像的准确建模或 噪声方差仍然是高质量解决方案的关键。学习的过程 对于未知的噪声分布，问题是不适定的。本论文研究的是 单联合图像去噪和噪声方差估计的任务 学习框架。为了解决这个问题的不妥之处，我们提出了 深度变异先验(DVP)，它表明适当的变异 对于噪声的变化，学习去噪器满足一定的光滑性 性能，作为良好去噪剂的关键标准。在DVP的基础上， 无监督深度学习框架，它同时学习去噪器和 估计噪声方差，是发展起来的。我们的方法不需要任何干净的 训练图像或噪声估计的外部步骤，并且取而代之， 仅使用一组噪声来逼近最小均方误差去噪器 图像。由于在单个框架中考虑两个基本任务， 我们允许它们相互优化。实验结果表明，该方法具有较好的性能。 去噪质量可与有监督学习和精确噪声相媲美 方差估计。