Toward Efficient Raw Image Denoising with Foundation Models
Summary
Deep-learning based denoising models are used to replace traditional denoising methods because of their better generalization ability and accuracy. Generating realistic pair wise data is important for the accuracy of
these deep denoising models on real-world noisy sceneries. Most deep denoising works are focussed on the accuracy of the model, not taking the
efficiency into account. Transformer models are the state-of-the-art performing denoising models. These transformer models are computationally
too heavy for real-time denoising. Knowledge distillation can be used for
compressing these models without losing much of the accuracy performance. We show that training deep denoising models on real-world noise
model image pairs results in a good performance on the generated test set,
and on real sensor noise image. Further, we show that the teacher-student
architecture with knowledge distillation improves the accuracy of the student network. These student models gain a lot of efficiency without losing
much of the teacher model accuracy, creating a better efficiency-accuracy
trade-off for real-world image denoising.