InverseNet: Solving Inverse Problems with Splitting Networks

TL;DR

InverseNet introduces a deep learning approach that decomposes inverse problem solving into two jointly trained networks, one for inversion and one for denoising, enabling flexible and efficient solutions for tasks like deblurring, super-resolution, and colorization.

Contribution

The paper presents a novel splitting network architecture for inverse problems, combining inversion and denoising networks trained end-to-end, improving flexibility and performance over existing methods.

Findings

Effective on synthetic and real datasets

Outperforms existing algorithms in accuracy

Versatile across multiple inverse tasks

Abstract

We propose a new method that uses deep learning techniques to solve the inverse problems. The inverse problem is cast in the form of learning an end-to-end mapping from observed data to the ground-truth. Inspired by the splitting strategy widely used in regularized iterative algorithm to tackle inverse problems, the mapping is decomposed into two networks, with one handling the inversion of the physical forward model associated with the data term and one handling the denoising of the output from the former network, i.e., the inverted version, associated with the prior/regularization term. The two networks are trained jointly to learn the end-to-end mapping, getting rid of a two-step training. The training is annealing as the intermediate variable between these two networks bridges the gap between the input (the degraded version of output) and output and progressively approaches to the ground-truth. The proposed network, referred to as InverseNet, is flexible in the sense that most of the existing end-to-end network structure can be leveraged in the first network and most of the existing denoising network structure can be used in the second one. Extensive experiments on both synthetic data and real datasets on the tasks, motion deblurring, super-resolution, and colorization, demonstrate the efficiency and accuracy of the proposed method compared with other image processing algorithms.