论文标题
无监督的深视频
Unsupervised Deep Video Denoising
论文作者
论文摘要
假设有干净的视频的可用性，用于视频降级的深度卷积神经网络（CNN）通常会受到监督的培训。但是，在许多应用程序（例如显微镜）中，没有噪音的视频。为了解决这个问题，我们提出了一个无监督的深视频Denoiser（UDVD），这是一种CNN体系结构，旨在专门使用嘈杂的数据进行培训。即使仅在单个简短的噪声视频中接受培训，UDVD的性能也可以与受监督的最先进的表现相当。我们通过降低原始视频，荧光 - 微观镜和电子微观镜数据来证明我们在现实世界成像应用中的方法的希望。与当前视频降解的许多方法相反，UDVD不需要明确的运动补偿。这是有利的，因为运动补偿在计算上是昂贵的，并且当输入数据嘈杂时可能不可靠。基于梯度的分析表明，UDVD会自动适应输入噪声视频中的本地运动。因此，即使仅接受了deno的训练，该网络也学会执行隐式运动补偿。
Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such as microscopy, noiseless videos are not available. To address this, we propose an Unsupervised Deep Video Denoiser (UDVD), a CNN architecture designed to be trained exclusively with noisy data. The performance of UDVD is comparable to the supervised state-of-the-art, even when trained only on a single short noisy video. We demonstrate the promise of our approach in real-world imaging applications by denoising raw video, fluorescence-microscopy and electron-microscopy data. In contrast to many current approaches to video denoising, UDVD does not require explicit motion compensation. This is advantageous because motion compensation is computationally expensive, and can be unreliable when the input data are noisy. A gradient-based analysis reveals that UDVD automatically adapts to local motion in the input noisy videos. Thus, the network learns to perform implicit motion compensation, even though it is only trained for denoising.
