论文标题
成像中的深度学习技术
Deep Learning Techniques for Inverse Problems in Imaging
论文作者
论文摘要
机器学习中的最新工作表明，深层神经网络可用于解决计算成像中引起的各种反问题。我们探讨了该新兴领域的中心流行主题，并提出了一种分类法，可用于分类不同的问题和重建方法。我们的分类法是沿两个中心轴组织的：（1）是否已知前向模型以及在训练和测试中使用了多大程度，以及（2）学习是否是监督或不监督的，即培训是否依赖于访问匹配的地面真实图像和测量对的访问。我们还讨论了与这些不同的重建方法，警告和常见失败模式相关的权衡，以及未来工作的开放问题和途径。
Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the trade-offs associated with these different reconstruction approaches, caveats and common failure modes, plus open problems and avenues for future work.
