  The performance of learning-based denoising largely depends on clean
supervision. However, it is difficult to obtain clean images in many scenes. On
the contrary, the capture of multiple noisy frames for the same field of view
is available and often natural in real life. Therefore, it is necessary to
avoid the restriction of clean labels and make full use of noisy data for model
training. So we propose an unsupervised learning strategy named one-pot
denoising (OPD) for multi-frame images. OPD is the first proposed unsupervised
multi-frame denoising (MFD) method. Different from the traditional supervision
schemes including both supervised Noise2Clean (N2C) and unsupervised
Noise2Noise (N2N), OPD executes mutual supervision among all of the multiple
frames, which gives learning more diversity of supervision and allows models to
mine deeper into the correlation among frames. N2N has also been proved to be
actually a simplified case of the proposed OPD. From the perspectives of data
allocation and loss function, two specific implementations, random coupling
(RC) and alienation loss (AL), are respectively provided to accomplish OPD
during model training. In practice, our experiments demonstrate that OPD
behaves as the SOTA unsupervised denoising method and is comparable to
supervised N2C methods for synthetic Gaussian and Poisson noise, and real-world
optical coherence tomography (OCT) speckle noise.
