论文标题
使用深图像先验和学习的重建方法的计算机断层扫描重建
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods
论文作者
论文摘要
在这项工作中，我们调查了在具有低DATA制度的背景下，深度学习方法在计算机断层扫描中的应用。作为动机，我们回顾了一些现有方法，并在培训不同量的数据后获得定量结果。我们发现，在重建质量和数据效率方面，学到的原始偶对偶会出色。但是，总的来说，端到端学习的方法有两个问题：a）在不使用足够数据培训的情况下，缺乏经典保证和b）缺乏概括。为了克服这些问题，我们引入了深层图像与经典正则化的结合。提出的方法改善了最新的数据导致数据测量使得。
In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime. As motivation, we review some of the existing approaches and obtain quantitative results after training them with different amounts of data. We find that the learned primal-dual has an outstanding performance in terms of reconstruction quality and data efficiency. However, in general, end-to-end learned methods have two issues: a) lack of classical guarantees in inverse problems and b) lack of generalization when not trained with enough data. To overcome these issues, we bring in the deep image prior approach in combination with classical regularization. The proposed methods improve the state-of-the-art results in the low data-regime.
