A Scalable Training Strategy for Blind Multi-Distribution Noise Removal
Kevin Zhang Sakshum Kulshrestha Christopher Metzler
Despite recent advances, developing general-purpose universal denoising and
artifact-removal networks remains largely an open problem: Given fixed network
weights, one inherently trades-off specialization at one task (e.g.,~removing
Poisson noise) for performance at another (e.g.,~removing speckle noise). In
addition, training such a network is challenging due to the curse of
dimensionality: As one increases the dimensions of the specification-space
(i.e.,~the number of parameters needed to describe the noise distribution) the
number of unique specifications one needs to train for grows exponentially.
Uniformly sampling this space will result in a network that does well at very
challenging problem specifications but poorly at easy problem specifications,
where even large errors will have a small effect on the overall mean squared
error.
  In this work we propose training denoising networks using an
adaptive-sampling/active-learning strategy. Our work improves upon a recently
proposed universal denoiser training strategy by extending these results to
higher dimensions and by incorporating a polynomial approximation of the true
specification-loss landscape. This approximation allows us to reduce training
times by almost two orders of magnitude. We test our method on simulated joint
Poisson-Gaussian-Speckle noise and demonstrate that with our proposed training
strategy, a single blind, generalist denoiser network can achieve peak
signal-to-noise ratios within a uniform bound of specialized denoiser networks
across a large range of operating conditions. We also capture a small dataset
of images with varying amounts of joint Poisson-Gaussian-Speckle noise and
demonstrate that a universal denoiser trained using our adaptive-sampling
strategy outperforms uniformly trained baselines.