A Multi-Image Restoration Method for Image Reconstruction from Projections

Traditional Bayesian restoration methods depend heavily on the accuracy of underlying generative models. For the challenging streak noise generated in the procedure of reconstruction from projections, Bayesian methods do not generalize well because accurate signal/noise models are not readily available. In this paper, we reformulate the reconstruction problem into a multi-image based restoration task and demonstrate that multiple images and mutual independence analysis can be utilized to significantly improve the generalization capability of traditional Bayesian frameworks in challenging scenarios. An efficient mutual independence analysis term is designed based on the properties of independent random variables to enforce the independent noise constraint between multiple images in an energy optimization framework, which can effectively detect and correct restoration error due to inaccurate generative models. Quantitative comparisons on phantom image and experiments on clinical scans both show significant improvements in accuracy and robustness of the proposed method.