论文标题
盲人多帧视频Denoising的自学培训
Self-Supervised training for blind multi-frame video denoising
论文作者
论文摘要
我们提出了一种自我监督的方法，用于培训多框架视频Denoising网络。这些网络可以从t框架周围的框架窗口预测帧t。我们的自我监督方法通过对预测的框架T和相邻目标框架之间的损失进行损失，从而从视频时间的一致性中受益，这些目标框架使用光流对齐。我们使用拟议的在线内部学习策略，在线训练的网络经过微调，以从单个视频中降低一种新的未知噪声类型。经过几帧之后，提议的微调可以达到，有时会超过接受监督训练的最先进网络的性能。此外，对于多种噪声类型，可以盲目应用它而不知道噪声分布。我们通过展示有关不同合成和现实噪声的盲目结果来证明这一点。
We propose a self-supervised approach for training multi-frame video denoising networks. These networks predict frame t from a window of frames around t. Our self-supervised approach benefits from the video temporal consistency by penalizing a loss between the predicted frame t and a neighboring target frame, which are aligned using an optical flow. We use the proposed strategy for online internal learning, where a pre-trained network is fine-tuned to denoise a new unknown noise type from a single video. After a few frames, the proposed fine-tuning reaches and sometimes surpasses the performance of a state-of-the-art network trained with supervision. In addition, for a wide range of noise types, it can be applied blindly without knowing the noise distribution. We demonstrate this by showing results on blind denoising of different synthetic and realistic noises.
