  Image denoising aims to restore a clean image from an observed noisy image.
The model-based image denoising approaches can achieve good generalization
ability over different noise levels and are with high interpretability.
Learning-based approaches are able to achieve better results, but usually with
weaker generalization ability and interpretability. In this paper, we propose a
wavelet-inspired invertible network (WINNet) to combine the merits of the
wavelet-based approaches and learningbased approaches. The proposed WINNet
consists of K-scale of lifting inspired invertible neural networks (LINNs) and
sparsity-driven denoising networks together with a noise estimation network.
The network architecture of LINNs is inspired by the lifting scheme in
wavelets. LINNs are used to learn a non-linear redundant transform with perfect
reconstruction property to facilitate noise removal. The denoising network
implements a sparse coding process for denoising. The noise estimation network
estimates the noise level from the input image which will be used to adaptively
adjust the soft-thresholds in LINNs. The forward transform of LINNs produce a
redundant multi-scale representation for denoising. The denoised image is
reconstructed using the inverse transform of LINNs with the denoised detail
channels and the original coarse channel. The simulation results show that the
proposed WINNet method is highly interpretable and has strong generalization
ability to unseen noise levels. It also achieves competitive results in the
non-blind/blind image denoising and in image deblurring.
