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.
翻译:由于最近的深层学习基础方法在清除图像噪音方面显示出有希望的结果,因此在监督的学习结构中报告了最佳分化业绩,这需要大量对齐的噪音图像和实地真相来进行培训。强有力的数据要求可以通过未经监督的学习技术来缓解,但是,对图像的准确建模或噪音差异对于高质量的解决方案仍然至关重要。学习问题对于未知噪音的传播来说是没有根据的。本文件在一个联合学习的单一框架内调查图像分解和噪音差异估计的任务。为了解决这个问题的不正确性能,我们在之前(DVP)提出了深刻的变异(DVP),其中指出,在噪音变化方面适当学习的脱义师的变异性符合一些光滑性特性,作为良好的食堂器的关键标准。在DVP的基础上,一个未经监督的深层学习框架,在学习消音器和估计噪音差异的同时学习。我们的方法不需要任何清洁的训练图像或外部的噪音估计步骤,相反,我们只是用一套焦度的图像来估计最低的平方差性差,而每个只使用一套平面图像来估计。我们考虑的实验结果。两个基础的实验结果,让我们进行对比。