Computational Optical Imaging: Methodology and Applications
A special issue of Photonics (ISSN 2304-6732).
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 274
Special Issue Editors
Interests: computational microscopy; phase retrieval; non-convex optimization; deep learning
Interests: computational imaging; quantitative phase microscopy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The advent of computational optical imaging has revolutionized the field of imaging sciences by integrating cutting-edge computational algorithms with traditional imaging hardware. This Special Issue aims to present the latest advancements regarding both techniques and applications in the field of computational optical imaging. It ranges in scope from methodology innovations in the field of quantitative phase imaging, volumetric imaging, and hyperspectral imaging, among others, to significant applications, e.g., biomedical studies and computer vision. By merging advanced signal processing, machine learning, and optics, computational optical imaging has opened up new possibilities for imaging systems, enabling enhanced image acquisition, analysis, and understanding.
Dr. Cheng Shen
Dr. An Pan
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- quantitative phase imaging
- wavefront coding
- hyperspectral imaging
- single pixel imaging and compressive sensing
- 3D sensing and volumetric reconstruction
- computer-generated holography
- non-line-of-sight imaging
- spatial and temporal optical super-resolution
- deep learning for computational imaging
