Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration.

Journal: Computers in biology and medicine
PMID: 39442443

Abstract

BACKGROUND AND OBJECTIVE: For the augmented reality of surgical navigation, which overlays a 3D model of the surgical target on an image, accurate camera calibration is imperative. However, when the checkerboard images for calibration are captured using a surgical microscope having high magnification, blur owing to the narrow depth of focus and blocking artifacts caused by limited resolution around the fine edges occur. These artifacts strongly affect the localization of corner points of the checkerboard in these images, resulting in inaccurate calibration, which leads to a large displacement in augmented reality. To solve this problem, in this study, we proposed a novel target-specific deep learning network that simultaneously enhances both the blur and spatial resolution of an image for surgical zoom lens camera calibration.

Authors

  • Ho-Gun Ha
    Division of Intelligent Robot, DGIST, 333 Techno Jungang-daero, Hyeonpung-myeon, Dalseong-Gun, Daegu 42988, Republic of Korea. Electronic address: hogus@dgist.ac.kr.
  • Deokgi Jeung
    Department of Robotics and Mechatronics Engineering, DGIST, 333 Techno Jungang-daero, Hyeonpung-myeon, Dalseong-gun, Daegu, 42988, Republic of Korea.
  • Ihsan Ullah
    Department of Robotics and Mechatronics Engineering, Daegu Gyeonbuk Institute of Science and Engineering (DGIST), Daegu, 42988, South Korea.
  • Junichi Tokuda
  • Jaesung Hong
    Department of Robotics Engineering, DGIST, 333, Techno jungang-daero, Hyeonpung-myeon, Dalseong-gun, Daegu, Korea. jhong@dgist.ac.kr.
  • Hyunki Lee
    Division of Intelligent Robot, DGIST, 333 Techno Jungang-daero, Hyeonpung-myeon, Dalseong-gun, Daegu, 42988, Republic of Korea. Electronic address: hklee@dgist.ac.kr.

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