标题： 对求解线性逆问题的展开网络的综合研究 显示英文标题

标题： Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems

摘要： 未展开的网络在各种计算机视觉和成像任务中变得普遍。 尽管它们在解决特定的计算机视觉和计算成像任务方面表现出色，但将其适应到其他应用中仍面临巨大挑战。 这主要是由于从事新应用的研究人员必须处理大量的设计决策，每个决策都可能影响网络的整体性能。 这些决策包括选择优化算法、定义损失函数以及确定卷积层的数量等。 此外，评估每个设计选择都需要耗时的模拟来训练、微调神经网络并优化其性能。 因此，探索多种选项并找到最佳配置的过程变得耗时且计算成本高。 本文的主要目标是（1）统一一些在未展开网络中使用的思想和方法，以减少用户需要做出的设计选择数量，以及（2）报告一项全面的消融研究，讨论设计未展开网络过程中每个选择的影响，并基于我们的发现提出实际的建议。 我们预计这项研究将有助于科学家和工程师为他们的应用设计未展开网络，并高效地诊断网络中的问题。 显示英文摘要

摘要： Unrolled networks have become prevalent in various computer vision and imaging tasks. 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, defining the loss function, and determining the number of convolutional layers, among others. Compounding the issue, evaluating each design choice requires time-consuming simulations to train, fine-tune the neural network, and optimize for its performance. 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 ablation study to discuss the impact of each of the choices involved in designing unrolled networks and present practical recommendations based on our findings. We anticipate that this study will help scientists and engineers design unrolled networks for their applications and diagnose problems within their networks efficiently.