  Image denoising is of vital importance in many imaging or computer vision
related areas. With the convolutional neural networks showing strong capability
in computer vision tasks, the performance of image denoising has also been
brought up by CNN based methods. Though CNN based image denoisers show
promising results on this task, most of the current CNN based methods try to
learn the mapping from noisy image to clean image directly, which lacks the
explicit exploration of prior knowledge of images and noises. Natural images
are observed to obey the reciprocal power law, implying the low-frequency band
of image tend to occupy most of the energy. Thus in the condition of AGWN
(additive gaussian white noise) deterioration, low-frequency band tend to
preserve a higher PSNR than high-frequency band. Considering the spatial
morphological consistency of different frequency bands, low-frequency band with
more fidelity can be used as a guidance to refine the more contaminated
high-frequency bands. Based on this thought, we proposed a novel network
architecture denoted as IGNet, in order to refine the frequency bands from low
to high in a progressive manner. Firstly, it decomposes the feature maps into
high- and low-frequency subbands using DWT (discrete wavelet transform)
iteratively, and then each low band features are used to refine the high band
features. Finally, the refined feature maps are processed by a decoder to
recover the clean result. With this design, more inter-frequency prior and
information are utilized, thus the model size can be lightened while still
perserves competitive results. Experiments on several public datasets show that
our model obtains competitive performance comparing with other state-of-the-art
methods yet with a lightweight structure.
