论文标题
通过正规化降解的联合重建和校准
Joint Reconstruction and Calibration using Regularization by Denoising
论文作者
论文摘要
通过使用denoing（红色）正规化是一个广泛适用的框架，用于通过使用指定为DeNoisers的先验来解决反问题。尽管已显示RED在许多应用程序中提供了最先进的性能，但现有的RED算法需要确切了解成像系统的测量操作员，从而将其适用性限制在测量运算符具有参数不确定性的问题中。我们提出了一种称为校准红色（CAL-RED）的新方法，该方法可以对测量算子进行联合校准以及未知图像的重建。 Cal-Red将传统的红色方法扩展到需要校准测量操作员的成像问题。我们验证了在扰动投影角度下计算机断层扫描（CT）中图像重建问题的问题。我们的结果证实了使用预先训练的深层DeNoiser作为图像先验的CAL-RED对关节校准和重建的有效性。
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 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.
