Computational Imaging and Artificial Intelligence: The Next Revolution
of Mobile Vision
- URL: http://arxiv.org/abs/2109.08880v1
- Date: Sat, 18 Sep 2021 08:47:08 GMT
- Title: Computational Imaging and Artificial Intelligence: The Next Revolution
of Mobile Vision
- Authors: Jinli Suo, Weihang Zhang, Jin Gong, Xin Yuan, David J. Brady, Qionghai
Dai
- Abstract summary: Computational Imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems.
This work first introduces the advances of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI.
We propose a framework to deeply integrate CI and AI by using the example of self-driving vehicles with high-speed communication, edge computing and traffic planning.
- Score: 42.986246806259764
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Signal capture stands in the forefront to perceive and understand the
environment and thus imaging plays the pivotal role in mobile vision. Recent
explosive progresses in Artificial Intelligence (AI) have shown great potential
to develop advanced mobile platforms with new imaging devices. Traditional
imaging systems based on the "capturing images first and processing afterwards"
mechanism cannot meet this unprecedented demand. Differently, Computational
Imaging (CI) systems are designed to capture high-dimensional data in an
encoded manner to provide more information for mobile vision systems.Thanks to
AI, CI can now be used in real systems by integrating deep learning algorithms
into the mobile vision platform to achieve the closed loop of intelligent
acquisition, processing and decision making, thus leading to the next
revolution of mobile vision.Starting from the history of mobile vision using
digital cameras, this work first introduces the advances of CI in diverse
applications and then conducts a comprehensive review of current research
topics combining CI and AI. Motivated by the fact that most existing studies
only loosely connect CI and AI (usually using AI to improve the performance of
CI and only limited works have deeply connected them), in this work, we propose
a framework to deeply integrate CI and AI by using the example of self-driving
vehicles with high-speed communication, edge computing and traffic planning.
Finally, we outlook the future of CI plus AI by investigating new materials,
brain science and new computing techniques to shed light on new directions of
mobile vision systems.
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