Image Reconstruction by Splitting Deep Learning Regularization from Iterative Inversion [chapter]

Jiulong Liu, Tao Kuang, Xiaoqun Zhang
<span title="">2018</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
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
more &raquo; ... consistence will be fed into some chosen denoising 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 representation/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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-00928-1_26">doi:10.1007/978-3-030-00928-1_26</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qefyg7boqjc4lkrsg62kbvsf4q">fatcat:qefyg7boqjc4lkrsg62kbvsf4q</a> </span>
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