CoIL：基于坐标的成像反问题内部学习
CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
摘要：
我们提出了基于坐标的内部学习（CoIL）作为一种新的深度学习（DL）方法，用于测量的连续表示。与学习从测量到所需图像的映射的传统DL方法不同，CoIL训练多层感知器（MLP）通过将测量的坐标映射到其响应来编码完整的测量场。CoIL是一种自监督方法，除了测试对象本身的测量之外，不需要训练示例。一旦训练了MLP，CoIL就会生成新的测量值，这些测量值可以在大多数图像重建方法中使用。我们使用几种广泛使用的重建方法，包括纯粹基于模型的方法和基于DL的方法，在稀疏视图计算机断层扫描上验证CoIL。我们的研究结果表明，线圈的能力，不断提高所有考虑的方法，通过提供高保真度的测量领域的性能。
英文摘要：
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements. Unlike traditional DL methods that learn a mapping from the measurements to the desired image, CoIL trains a multilayer perceptron (MLP) to encode the complete measurement field by mapping the coordinates of the measurements to their responses. CoIL is a self-supervised method that requires no training examples besides the measurements of the test object itself. Once the MLP is trained, CoIL generates new measurements that can be used within a majority of image reconstruction methods. We validate CoIL on sparse-view computed tomography using several widely-used reconstruction methods, including purely model-based methods and those based on DL. Our results demonstrate the ability of CoIL to consistently improve the performance of all the considered methods by providing high-fidelity measurement fields.
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