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Yu Sun, Jiaming Liu, Mingyang Xie, Brendt Wohlberg and Ulugbek S. Kamilov, "CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems", arXiv, 2102.05181, 2021


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.

BibTeX Entry

author = {Yu Sun and Jiaming Liu and Mingyang Xie and Brendt Wohlberg and Ulugbek S. Kamilov},
title = {{CoIL}: Coordinate-based Internal Learning for Imaging Inverse Problems},
year = {2021},
month = Feb,
urlpdf = {},
urlhtml = {},
institution = {arXiv},
number = {2102.05181}