Computational Microscopy is a rapidly evolving field that combines efficient algorithms with non-conventional optical setups to enable high-resolution imaging performance. The improvements due to Computational Microscopy can be in terms of more cost-effective optical hardware, finer optical resolution, deeper imaging depth in scattering and aberrant specimens, and faster data acquisition in microscopy, to cite a few ongoing research areas. In addition, while some of Computational Microscopy approaches use physical models to enhance performance, more recent methods exploiting Machine Learning have gained popularity and have shown impressive results in this broadly defined field. This Special Issue is devoted to novel approaches in Computational Microscopy covering the following themes proposed (but not limited to):
Imaging performance enhancement using machine learning techniques (super-resolution, image restoration, registration, and sensor calibration).
Super-resolution microscopy by means of computational reconstruction (Structured Illumination Microscopy (SIM), Stochastic techniques (PALM, STORM)).
Compressive sensing microscopy and microspectroscopy (brightfield, Raman Scattering-based methods (spontaneous and coherent), infrared absorption, near-field scattering techniques).
High-resolution microscopy through scattering media via computational methods.
Computational microscopy techniques based on phase retrieval (Fourier Ptychography, Coherent X-ray Microscopy)
Dr. Hilton Barbosa de Aguiar, Ecole Normale Supérieure/Paris, France
Prof. Ulugbek Kamilov, Washington University in St. Louis, United States of America
Prof. Lei Tian, Boston University, United States of America
Submission deadline: December 31st 2020
Acceptance deadline: March 31st 2021