Traditional supervised denoisers are trained using pairs of noisy input and clean target images. They learn to predict a central tendency of the posterior distribution over possible clean images. When, e.g., trained with the popular quadratic loss function, the network's output will correspond to the minimum mean square error (MMSE) estimate. Unsupervised denoisers based on Variational AutoEncoders (VAEs) have succeeded in achieving state-of-the-art results while requiring only unpaired noisy data as training input. In contrast to the traditional supervised approach, unsupervised denoisers do not directly produce a single prediction, such as the MMSE estimate, but allow us to draw samples from the posterior distribution of clean solutions corresponding to the noisy input. To approximate the MMSE estimate during inference, unsupervised methods have to create and draw a large number of samples - a computationally expensive process - rendering the approach inapplicable in many situations. Here, we present an alternative approach that trains a deterministic network alongside the VAE to directly predict a central tendency. Our method achieves results that surpass the results achieved by the unsupervised method at a fraction of the computational cost.

翻译：传统的监督式去噪器通过使用成对的含噪输入和清晰目标图像进行训练，学习预测清晰图像后验分布的中心趋势。例如，当使用流行的二次损失函数训练时，网络输出将对应最小均方误差（MMSE）估计。基于变分自编码器（VAE）的无监督去噪器已成功取得最先进结果，且只需未配对的含噪数据作为训练输入。与传统监督方法不同，无监督去噪器不直接产生单一预测（如MMSE估计），而是允许我们从与含噪输入对应的清晰解后验分布中采样。为在推理阶段近似MMSE估计，无监督方法需创建并抽取大量样本——这一计算成本高昂的过程——导致该方法在许多情况下无法应用。本文提出一种替代方案，在VAE旁联合训练一个确定性网络，直接预测中心趋势。我们的方法能以极低的计算成本超越无监督方法所取得的结果。