High Dynamic Range Image Synthesis Strategy for Automotive Cameras.

Saved in:

Bibliographic Details
Title:	High Dynamic Range Image Synthesis Strategy for Automotive Cameras.
Authors:	Xu, Xiangyang¹, Li, Chan¹, Xu, hiting², Zheng, Caiyun², Zhang, hicheng¹ zhangzhch8@szpu.edu.cn
Source:	Journal of Imaging Science & Technology. Jan/Feb2026, Vol. 70 Issue 1, p1-8. 8p.
Subjects:	High dynamic range imaging, Image quality analysis, Automobile rear view cameras, Wave amplification, Driver assistance systems, Autonomous vehicles
Abstract:	High dynamic range (HDR) imaging techniques effectively enhance image quality in driving scenarios. However, HDR image synthesis in automotive cameras remains challenging due to complex conditions such as high-contrast scenes, low-light environments, and vehicle motion. Automotive cameras typically optimize image quality by adjusting exposure time to control the duration of light capture and tuning analog gain to amplify the sensitivity of the imaging sensor. The study aims to identify the optimal parameter combinations of exposure times and analog gain settings for HDR image synthesis in automotive cameras. The authors acquire images at three frames captured under different exposure time and analog gain combinations and use HDR image synthesis techniques to simulate HDR images. A composite quality evaluation method was established based on four dimensions: tone range, tone levels, contrast, and signal-to-noise ratio. Quantitative analysis revealed that the best HDR image quality was achieved when the exposure time ranged from 5 ms to 20 ms and the analog gain ranged from 1x to 2x. The proposed HDR image synthesis strategy demonstrates significant practical value for automotive vision systems, improving image quality and providing more accurate and reliable visual information for autonomous driving and advanced driver-assistance systems, enhancing driving safety and user experience. [ABSTRACT FROM AUTHOR]
	Copyright of Journal of Imaging Science & Technology is the property of International Society for Imaging Science & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	High dynamic range (HDR) imaging techniques effectively enhance image quality in driving scenarios. However, HDR image synthesis in automotive cameras remains challenging due to complex conditions such as high-contrast scenes, low-light environments, and vehicle motion. Automotive cameras typically optimize image quality by adjusting exposure time to control the duration of light capture and tuning analog gain to amplify the sensitivity of the imaging sensor. The study aims to identify the optimal parameter combinations of exposure times and analog gain settings for HDR image synthesis in automotive cameras. The authors acquire images at three frames captured under different exposure time and analog gain combinations and use HDR image synthesis techniques to simulate HDR images. A composite quality evaluation method was established based on four dimensions: tone range, tone levels, contrast, and signal-to-noise ratio. Quantitative analysis revealed that the best HDR image quality was achieved when the exposure time ranged from 5 ms to 20 ms and the analog gain ranged from 1x to 2x. The proposed HDR image synthesis strategy demonstrates significant practical value for automotive vision systems, improving image quality and providing more accurate and reliable visual information for autonomous driving and advanced driver-assistance systems, enhancing driving safety and user experience. [ABSTRACT FROM AUTHOR]
ISSN:	10623701
DOI:	10.2352/J.ImagingSci.Technol.2026.70.1.010407