Imaging, be it in optics, acoustics, RF, X-ray, THz, NMR (Nuclear Magnetic Resonance), etc., has witnessed tremendous progress in the last decades, driven not only by technical advances (new detectors, modulators, sources), but also by the revolution in signal processing and machine learning of the last two decades, from compressive sensing, phase-retrieval methods, to deep learning, to cite just a few. The field of “computational imaging” has emerged in the last 10 years as a very powerful paradigm, where advanced algorithms and hardware worked in synchrony to provide revolutionary advances in imaging, and strongly impacted many fields, including optical microscopy, astronomy, X-ray ptychography, MRI, radar, biology and medicine, seismology, etc.
The use of these modern computational methods has strongly improved imaging performance, be it in terms of resolution, signal, or speed. It has also sometimes simplified hardware tremendously by moving the burden of imaging to the algorithmic side. Finally, it has allowed imaging in scenarios that were considered impossible, for instance, imaging in complex media, around corner, or passive imaging.
This special issue aims at providing a comprehensive panorama of the more modern developments in this very active field, comprising both original research articles and reviews by world-renowned experts in the field.
Sylvain Gigan, Sorbonne Université, Paris, France (Editorial Board Member of Intelligent Computing)
Hilton Barbosa de Aguiar, CNRS, Paris, France
The timeline for the submission and review process is as follows (all deadlines are 23:59 (11:59PM) anywhere on earth).
- Invitation acceptance: 6 May 2022
- Paper submission: 29 July 2022
- Review notification: 16 September 2022
- Reviewed submission: 16 October 2022
Table of Contents
As articles within the special issue are published they will appear below.
Yuto Endo, Jun Tanida, Makoto Naruse, Ryoichi Horisaki
Intelligent Computing, vol. 2022, Article ID 9787098, 8 pages, 2022