  Invertible networks have various benefits for image denoising since they are
lightweight, information-lossless, and memory-saving during back-propagation.
However, applying invertible models to remove noise is challenging because the
input is noisy, and the reversed output is clean, following two different
distributions. We propose an invertible denoising network, InvDN, to address
this challenge. InvDN transforms the noisy input into a low-resolution clean
image and a latent representation containing noise. To discard noise and
restore the clean image, InvDN replaces the noisy latent representation with
another one sampled from a prior distribution during reversion. The denoising
performance of InvDN is better than all the existing competitive models,
achieving a new state-of-the-art result for the SIDD dataset while enjoying
less run time. Moreover, the size of InvDN is far smaller, only having 4.2% of
the number of parameters compared to the most recently proposed DANet. Further,
via manipulating the noisy latent representation, InvDN is also able to
generate noise more similar to the original one. Our code is available at:
https://github.com/Yang-Liu1082/InvDN.git.
