  The prevalent convolutional neural network (CNN) based image denoising
methods extract features of images to restore the clean ground truth, achieving
high denoising accuracy. However, these methods may ignore the underlying
distribution of clean images, inducing distortions or artifacts in denoising
results. This paper proposes a new perspective to treat image denoising as a
distribution learning and disentangling task. Since the noisy image
distribution can be viewed as a joint distribution of clean images and noise,
the denoised images can be obtained via manipulating the latent representations
to the clean counterpart. This paper also provides a distribution learning
based denoising framework. Following this framework, we present an invertible
denoising network, FDN, without any assumptions on either clean or noise
distributions, as well as a distribution disentanglement method. FDN learns the
distribution of noisy images, which is different from the previous CNN based
discriminative mapping. Experimental results demonstrate FDN's capacity to
remove synthetic additive white Gaussian noise (AWGN) on both category-specific
and remote sensing images. Furthermore, the performance of FDN surpasses that
of previously published methods in real image denoising with fewer parameters
and faster speed. Our code is available at:
https://github.com/Yang-Liu1082/FDN.git.
