  In this paper, we propose an invertible neural network for image denoising
(DnINN) inspired by the transform-based denoising framework. The proposed DnINN
consists of an invertible neural network called LINN whose architecture is
inspired by the lifting scheme in wavelet theory and a sparsity-driven
denoising network which is used to remove noise from the transform
coefficients. The denoising operation is performed with a single
soft-thresholding operation or with a learned iterative shrinkage thresholding
network. The forward pass of LINN produces an over-complete representation
which is more suitable for denoising. The denoised image is reconstructed using
the backward pass of LINN using the output of the denoising network. The
simulation results show that the proposed DnINN method achieves results
comparable to the DnCNN method while only requiring 1/4 of learnable
parameters.
