Multi-criterion reconstruction method for optical tomography

The reconstruction of optical tomography from measurement data is an ill-posed problem. For such an ill-posed problem, a multi-criterion strategy is proposed, in which three criteria of squared error function, image entropy and local smoothness function are used for the reconstruction. The multi-criterion reconstruction problem is transformed to a single criterion problem by using the vector optimization method. A dynamic weight coefficient solution is proposed to determine the weight coefficient of objective function. For the realization of optical tomography reconstruction, a gradient tree based algorithm is proposed for the gradient computation of the objective function with respect to optical parameters. Different results from the multi-criterion reconstruction and single criterion reconstruction based on the squared error function are presented and compared. Experimental results show that this overcomes the shortcoming of conventional single criterion reconstruction only depending on the single objective and is valid for optical tomography reconstruction and the image quality is significantly improved.