Deep Learning for Camera Autofocus

Journal Article (Journal Article)

Most digital cameras use specialized autofocus sensors, such as phase detection, lidar or ultrasound, to directly measure focus state. However, such sensors increase cost and complexity without directly optimizing final image quality. This paper proposes a new pipeline for image-based autofocus and shows that neural image analysis finds focus 5-10x faster than traditional contrast enhancement. We achieve this by learning the direct mapping between an image and its focus position. In further contrast with conventional methods, AI methods can generate scene-based focus trajectories that optimize synthesized image quality for dynamic and three dimensional scenes. We propose a focus control strategy that varies focal position dynamically to maximize image quality as estimated from the focal stack. We propose a rule-based agent and a learned agent for different scenarios and show their advantages over other focus stacking methods.

Full Text

Duke Authors

Cited Authors

  • Wang, C; Huang, Q; Cheng, M; Ma, Z; Brady, DJ

Published Date

  • January 1, 2021

Published In

Volume / Issue

  • 7 /

Start / End Page

  • 258 - 271

Electronic International Standard Serial Number (EISSN)

  • 2333-9403

International Standard Serial Number (ISSN)

  • 2573-0436

Digital Object Identifier (DOI)

  • 10.1109/TCI.2021.3059497

Citation Source

  • Scopus