Computational Imaging Through Atmospheric Turbulence

• Published in: arXiv.org
• Main Authors: Chimitt, Nicholas, Chan, Stanley H
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 01.11.2024
• Subjects: Atmospheric turbulence; Computer vision; Deep learning; Engineering education; Image processing; Image reconstruction; Space missions
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2411.00338

Since the seminal work of Andrey Kolmogorov in the early 1940's, imaging through atmospheric turbulence has grown from a pure scientific pursuit to an important subject across a multitude of civilian, space-mission, and national security applications. Fueled by the recent advancement of deep learning, the field is further experiencing a new wave of momentum. However, for these deep learning methods to perform well, new efforts are needed to build faster and more accurate computational models while at the same time maximizing the performance of image reconstruction. The book is written primarily for image processing engineers, computer vision scientists, and engineering students who are interested in the field of atmospheric turbulence, statistical optics, and image processing. The book can be used as a graduate text, or advanced topic classes for undergraduates.