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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", IEEE Transactions on Computational Imaging, vol. 7, doi:10.1109/TCI.2021.3125564, pp. 1400-1412, Nov 2021

Abstract

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

@article{yu-2021-coil,
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 = Nov,
urlpdf = {http://arxiv.org/pdf/2102.05181},
journal = {IEEE Transactions on Computational Imaging},
volume = {7},
doi = {10.1109/TCI.2021.3125564},
pages = {1400-1412}
}