5-Year Impact Factor:
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:
Multimodality Image Fusion