Deep Memory Unrolled Networks for Solving Imaging Linear Inverse Problems

Yuxi Chen, Xi Chen, Shirin Jalali, Arian Maleki

Session: General

Keywords: Compressed Sensing, Unrolled Networks, Computational Imaging

Abstract: Unrolled networks have emerged as one of the most successful methods in imaging applications. Although they have demonstrated remarkable efficacy in solving specific computer vision and computational imaging tasks, their adaptation to other applications presents considerable challenges. This is primarily due to the multitude of design decisions that practitioners working on new applications must navigate, each potentially affecting the network's overall performance. These decisions include selecting the optimization algorithm to unroll, defining the loss function, deciding on the structure of residual connections, and determining the number of convolutional layers, among others. Compounding the issue, evaluating each design choice requires time-consuming simulations to train the neural network. As a result, the process of exploring multiple options and identifying the optimal configuration becomes time-consuming and computationally demanding. The main objectives of this paper are (1) to unify some ideas and methodologies used in unrolled networks to reduce the number of design choices a user has to make, and (2) to report a comprehensive numerical study to discover the optimal design choices. We anticipate that this study will help scientists and engineers design unrolled networks for their applications and diagnose problems within their networks efficiently.

Submission Number: 34