  In this paper, we propose two contributions to neural network based
denoising. First, we propose applying separate convolutional layers to each
sub-band of discrete wavelet transform (DWT) as opposed to the common usage of
DWT which concatenates all sub-bands and applies a single convolution layer. We
show that our approach to using DWT in neural networks improves the accuracy
notably, due to keeping the sub-band order uncorrupted prior to inverse DWT.
Our second contribution is a denoising loss based on top k-percent of errors in
frequency domain. A neural network trained with this loss, adaptively focuses
on frequencies that it fails to recover the most in each iteration. We show
that this loss results into better perceptual quality by providing an image
that is more balanced in terms of the errors in frequency components.

Machine Learning;Computer Vision and Pattern Recognition