Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms
It provides a synthesis of research directions for researchers in scientific fields dealing with linear inverse problems, but it is incremental as it surveys existing work rather than introducing new methods.
This paper presents a comprehensive survey of recent progress in using deep learning methods to solve linear inverse problems, reviewing various network architectures and their state-of-the-art performance in applications.
The linear inverse problem is fundamental to the development of various scientific areas. Innumerable attempts have been carried out to solve different variants of the linear inverse problem in different applications. Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance in many applications. In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems. We review how deep learning methods are used in solving different linear inverse problems, and explore the structured neural network architectures that incorporate knowledge used in traditional methods. Furthermore, we identify open challenges and potential future directions along this research line.