LINN: Lifting Inspired Invertible Neural Network for Image Denoising
This is an incremental improvement for image processing, offering parameter efficiency in denoising tasks.
The paper tackles image denoising by proposing an invertible neural network (DnINN) inspired by wavelet lifting schemes, achieving results comparable to DnCNN with only 1/4 of the learnable parameters.
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.