Joint Reconstruction and Calibration Using Regularization by Denoising With Application to Computed Tomography

Mingyang Xie, Jiaming Liu, Yu Sun, Weijie Gan, Brendt Wohlberg, Ulugbek S. Kamilov; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 4028-4037

Abstract


Regularization by denoising (RED) is a broadly applicable framework for solving inverse problems by using priors specified as denoisers. While RED has been shown to provide state-of-the-art performance in a number of imaging applications, existing RED algorithms require exact knowledge of the measurement operator characterizing the imaging system, limiting their applicability in problems where the measurement operator has parametric uncertainties. We propose a new method, called Calibrated RED (Cal-RED), that enables joint calibration of the measurement operator along with reconstruction of the unknown image. Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator. We validate Cal-RED on the problem of image reconstruction in computerized tomography (CT) under perturbed projection angles. Our results corroborate the effectiveness of Cal-RED for joint calibration and reconstruction using pre-trained deep denoisers as image priors.

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[bibtex]
@InProceedings{Xie_2021_ICCV, author = {Xie, Mingyang and Liu, Jiaming and Sun, Yu and Gan, Weijie and Wohlberg, Brendt and Kamilov, Ulugbek S.}, title = {Joint Reconstruction and Calibration Using Regularization by Denoising With Application to Computed Tomography}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {4028-4037} }