Total Variation Regularization With Split Bregman-Based Method in Magnetic Induction Tomography Using Experimental Data

摘要: Magnetic induction tomography (MIT) is an imaging modality with a wide range of potential applications due to its non-contact nature. MIT member the electrical family that faces most difficult challenges, demanding measurement accuracy requirements and forward inverse problems. This paper presents for first time split Bregman total variation (TV) regularization solve problem. Comparative evaluations are presented between proposed TV algorithm more commonly used Tikhonov method. regularization, which based on ${l}_{2}{-norm}$ , solved linearly, while using formulation, has been shown be optimal ${l}_{1}{-norm}$ regularization. Experimental results quantified by number image quality measurements, show superiority method both low conductivity high data. Significant improvement in will make great candidate types imaging.