  Recent years have witnessed the great success of deep convolutional neural
networks (CNNs) in image denoising. Albeit deeper network and larger model
capacity generally benefit performance, it remains a challenging practical
issue to train a very deep image denoising network. Using multilevel
wavelet-CNN (MWCNN) as an example, we empirically find that the denoising
performance cannot be significantly improved by either increasing wavelet
decomposition levels or increasing convolution layers within each level. To
cope with this issue, this paper presents a multi-level wavelet residual
network (MWRN) architecture as well as a progressive training (PTMWRN) scheme
to improve image denoising performance. In contrast to MWCNN, our MWRN
introduces several residual blocks after each level of discrete wavelet
transform (DWT) and before inverse discrete wavelet transform (IDWT). For
easing the training difficulty, scale-specific loss is applied to each level of
MWRN by requiring the intermediate output to approximate the corresponding
wavelet subbands of ground-truth clean image. To ensure the effectiveness of
scale-specific loss, we also take the wavelet subbands of noisy image as the
input to each scale of the encoder. Furthermore, progressive training scheme is
adopted for better learning of MWRN by beigining with training the lowest level
of MWRN and progressively training the upper levels to bring more fine details
to denoising results. Experiments on both synthetic and real-world noisy images
show that our PT-MWRN performs favorably against the state-of-the-art denoising
methods in terms both quantitative metrics and visual quality.
