Kamyab Shima, Azimifar Zohreh, Sabzi Rasool, Fieguth Paul
Department of Computer Science and Engineering, Shiraz University, Shiraz, Fars, Iran.
Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada.
PeerJ Comput Sci. 2022 May 2;8:e951. doi: 10.7717/peerj-cs.951. eCollection 2022.
In this paper we investigate a variety of deep learning strategies for solving inverse problems. We classify existing deep learning solutions for inverse problems into three categories of Direct Mapping, Data Consistency Optimizer, and Deep Regularizer. We choose a sample of each inverse problem type, so as to compare the robustness of the three categories, and report a statistical analysis of their differences. We perform extensive experiments on the classic problem of linear regression and three well-known inverse problems in computer vision, namely image denoising, 3D human face inverse rendering, and object tracking, in presence of noise and outliers, are selected as representative prototypes for each class of inverse problems. The overall results and the statistical analyses show that the solution categories have a robustness behaviour dependent on the type of inverse problem domain, and specifically dependent on whether or not the problem includes measurement outliers. Based on our experimental results, we conclude by proposing the most robust solution category for each inverse problem class.
在本文中，我们研究了用于解决逆问题的各种深度学习策略。我们将现有的逆问题深度学习解决方案分为直接映射、数据一致性优化器和深度正则化器三类。我们为每种逆问题类型选择一个样本，以便比较这三类的鲁棒性，并报告它们差异的统计分析。我们对线性回归的经典问题以及计算机视觉中的三个著名逆问题进行了广泛实验，即图像去噪、3D 人脸逆渲染和目标跟踪，在存在噪声和离群值的情况下，将它们选为每类逆问题的代表性原型。总体结果和统计分析表明，这些解决方案类别具有取决于逆问题域类型的鲁棒性行为，具体取决于问题是否包含测量离群值。基于我们的实验结果，我们通过为每个逆问题类别提出最鲁棒的解决方案类别来得出结论。
