Image Reconstruction by Splitting Deep Learning Regularization from Iterative Inversion

摘要

Image reconstruction from downsampled and corrupted measurements, such as fast MRI and low dose CT, is mathematically ill-posed inverse problem. In this work, we propose a general and easy-to-use reconstruction method based on deep learning techniques. In order to address the intractable inversion of general inverse problems, we propose to train a network to refine intermediate images from classical reconstruction procedure to the ground truth, i.e. the intermediate images that satisfy the data consistence will be fed into some chosen denois-ing networks or generative networks for denoising and removing artifact in each iterative stage. The proposed approach involves only techniques of conventional image reconstruction and usual image representa-tion/denoising deep network learning, without a specifically designed and complicated network structures for a certain physical forward operator. Extensive experiments on MRI reconstruction applied with both stack auto-encoder networks and generative adversarial nets demonstrate the efficiency and accuracy of the proposed method compared with other image reconstruction algorithms.