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Stereo Vision-Based High Dynamic Range Imaging Using Differently-Exposed Image Pair.

Won-Jae Park1, Seo-Won Ji2, Seok-Jae Kang3

  • 1School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 136-701, Korea. wjpark@dali.korea.ac.kr.

Sensors (Basel, Switzerland)
|June 23, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel stereo vision method for high dynamic range (HDR) imaging. It effectively restores lost radiance in under/over-exposed regions, improving image quality for stereo cameras.

Keywords:
high dynamic range imaginghigh dynamic range reconstructionhole-fillingstereo matchingstereo vision system

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Photography

Background:

  • High dynamic range (HDR) imaging is crucial for capturing scenes with extreme lighting variations.
  • Existing methods struggle with limited dynamic range in stereo cameras, leading to data loss in extreme exposure regions.
  • Stereo vision systems offer potential for enhanced HDR imaging by leveraging multiple viewpoints.

Purpose of the Study:

  • To develop an advanced high dynamic range (HDR) imaging method utilizing stereo vision.
  • To address the challenge of lost radiance values in under/over-exposed regions of initial HDR images.
  • To improve the overall quality and detail preservation in HDR images captured by stereo cameras.

Main Methods:

  • Utilizing differently exposed low dynamic range (LDR) images from a stereo camera.
  • Converting LDR images to initial stereo HDR images via inverse camera response function estimation.
  • Applying stereo matching and hole-filling algorithms to restore lost radiance information.
  • Warping an auxiliary-view (AV) HDR image using estimated disparity and performing hole-filling.
  • Fusing the processed AV HDR image with the main-view (MV) HDR image using a weight map for final reconstruction.

Main Results:

  • Successfully restored radiance values in under/over-exposed regions of the main-view HDR image.
  • Demonstrated improved performance in both objective and subjective evaluations compared to conventional methods.
  • The proposed method effectively reconstructs a final MV HDR image with enhanced detail and dynamic range.

Conclusions:

  • The presented stereo vision-based HDR imaging method effectively overcomes limitations of conventional approaches.
  • The integration of stereo matching and hole-filling significantly enhances radiance restoration in challenging lighting conditions.
  • This technique offers a robust solution for generating high-quality HDR images from stereo camera systems.