Nest-DGIL : Nesterov-optimized deep geometric incremental learning for CS image reconstruction

Fan, Xiaohong and Yang, Yin and Chen, Ke and Feng, Yujie and Zhang, Jianping (2023) Nest-DGIL : Nesterov-optimized deep geometric incremental learning for CS image reconstruction. IEEE Transactions on Computational Imaging, 9. pp. 819-833. ISSN 2333-9403 (https://doi.org/10.1109/TCI.2023.3315853)

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Abstract

Proximal gradient-based optimization is one of the most common strategies to solve inverse problem of images, and it is easy to implement. However, these techniques often generate heavy artifacts in image reconstruction. One of the most popular refinement methods is to fine-tune the regularization parameter to alleviate such artifacts, but it may not always be sufficient or applicable due to increased computational costs. In this work, we propose a deep geometric incremental learning framework based on the second Nesterov proximal gradient optimization. The proposed end-to-end network not only has the powerful learning ability for high-/low-frequency image features, but also can theoretically guarantee that geometric texture details will be reconstructed from preliminary linear reconstruction. Furthermore, it can avoid the risk of intermediate reconstruction results falling outside the geometric decomposition domains and achieve fast convergence. Our reconstruction framework is decomposed into four modules including general linear reconstruction, cascade geometric incremental restoration, Nesterov acceleration, and post-processing. In the image restoration step, a cascade geometric incremental learning module is designed to compensate for missing texture information from different geometric spectral decomposition domains. Inspired by the overlap-tile strategy, we also develop a post-processing module to remove the block effect in patch-wise-based natural image reconstruction. All parameters in the proposed model are learnable, an adaptive initialization technique of physical parameters is also employed to make model flexibility and ensure converging smoothly. We compare the reconstruction performance of the proposed method with existing state-of-the-art methods to demonstrate its superiority.

  • Item type:Article
    ID code:86912
    Dates:
    Date
    Event
    15 September 2023
    Published
    12 September 2023
    Accepted
    Notes:Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:10 Oct 2023 14:27
    Last modified:18 Apr 2024 00:38
    URI:https://strathprints.strath.ac.uk/id/eprint/86912
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