Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision
Jinli Suo, Weihang Zhang, Jin Gong, Xin Yuan, David J. Brady, Qionghai, Dai

TL;DR
This paper reviews the integration of Computational Imaging and Artificial Intelligence in mobile vision, highlighting recent advances, proposing a deep integration framework, and exploring future directions for intelligent mobile imaging systems.
Contribution
It provides a comprehensive review of CI and AI in mobile vision and proposes a framework for their deep integration using self-driving vehicles as an example.
Findings
Deep integration of CI and AI enhances mobile vision capabilities.
AI enables real-time processing and decision-making in CI systems.
Future directions include new materials, brain science, and computing techniques.
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…
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Taxonomy
TopicsCCD and CMOS Imaging Sensors · Advanced Optical Sensing Technologies · Advanced Neural Network Applications
