News
  • Title: Machine Learning for Computational Imaging

    Guest Editors: Chenglong Bao (Tsinghua University), Bin Dong (Peking University), Jingwei Liang (Shanghai Jiao Tong University)

    Submission deadline: Nov 30, 2023

    Online date: June, 2024

     

    Mathematics, physics, and machine learning are crucial in computational imaging (CI). They are integral to various aspects of CI, such as image acquisition, reconstruction, and analysis. The constant advancements in CI necessitate the development of new imaging methodologies, hardware design, instrumental control, and the integration of advanced mathematical and machine-learning algorithms. The upcoming special issue will delve into the significant trends and challenges in this area, highlighting the latest research on mathematical model/scientific computing method-inspired machine learning approaches and their practical applications in CI.

     

    Topics of interest include but are not limited to machine learning methods (e.g., deep learning, reinforcement learning, statistical methods) for:

    1. Image Restoration

    2. Image Segmentation

    3. Compressed Sensing

    4. Multimodality Image Fusion

    5. Medical Imaging

    6. Optical Imaging

    7. Cryo-EM/Cryo-ET

    8. Super-Resolution Imaging

    9. Seismic imaging