论文标题
培训自适应重建网络针对盲人问题
Training Adaptive Reconstruction Networks for Blind Inverse Problems
论文作者
论文摘要
神经网络允许解决许多缺陷的逆问题，并具有前所未有的性能。物理知情的方法已经逐步替换了实际应用中精心设计的重建算法。但是，这些网络遭受了主要缺陷的困扰：当对给定的前向操作员进行培训时，它们并不能很好地推广到另一个。本文的目的是双重的。首先，我们通过各种应用显示，这些应用与一个远期操作员培训网络允许在不损害重建质量的情况下解决适应性问题。几何形状和图像脱毛，并用菲涅尔衍射核。
Neural networks allow solving many ill-posed inverse problems with unprecedented performance. Physics informed approaches already progressively replace carefully hand-crafted reconstruction algorithms in real applications. However, these networks suffer from a major defect: when trained on a given forward operator, they do not generalize well to a different one. The aim of this paper is twofold. First, we show through various applications that training the network with a family of forward operators allows solving the adaptivity problem without compromising the reconstruction quality significantly.Second, we illustrate that this training procedure allows tackling challenging blind inverse problems.Our experiments include partial Fourier sampling problems arising in magnetic resonance imaging (MRI) with sensitivity estimation and off-resonance effects, computerized tomography (CT) with a tilted geometry and image deblurring with Fresnel diffraction kernels.
