Abstract

It is well known that the image reconstruction problem in optical tomography is ill-posed. In this work we approach the problem within a gradient-based image iterative reconstruction (GIIR) scheme. The reconstruction is considered as a minimization of an objective function. This function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that contain a priori information about the system. In this work penalty functions are considered that are derived from full or partial knowledge of the histogram of the image to be reconstructed.

© 2000 Optical Society of America