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Contrast Enhancement Method Using Region-Based Dynamic Clipping Technique for LWIR-Based Thermal Camera of Night

Cheol-Ho Choi¹, Joonhwan Han¹, Jeongwoo Cha¹

¹Pangyo R&D Center, Hanwha Systems Co., Ltd., 188, Pangyoyeok-ro, Bundang-gu, Sengnam-si 13524, Gyeonggi-do, Republic of Korea.

Sensors (Basel, Switzerland)

|June 27, 2024

Summary

This study introduces a new contrast enhancement method for long-wave infrared (LWIR) thermal cameras used in autonomous driving. The technique improves image quality after non-uniformity (NUC) and temperature compensation (TC) processes.

Keywords:

contrast enhancement histogram equalization image processing infrared night vision thermal image

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Area of Science:

Computer Vision
Infrared Imaging
Autonomous Systems

Background:

Long-wave infrared (LWIR) uncooled thermal-imaging cameras are increasingly used in autonomous driving for robust data collection in diverse environments.
Existing research on contrast enhancement for LWIR cameras often overlooks post-processing steps like non-uniformity (NUC) and temperature compensation (TC).

Purpose of the Study:

To propose and evaluate a novel contrast enhancement method for LWIR thermal images specifically after NUC and TC processes.
To address the lack of experimental data on contrast enhancement applied to images generated by final production stages of LWIR cameras.

Main Methods:

A histogram equalization (HE)-based contrast enhancement method incorporating a region-based clipping technique was developed.
Experimental evaluations were performed on images processed through NUC and TC, followed by the proposed contrast enhancement.

Main Results:

The proposed method demonstrated improved image clarity and contrast ratio compared to conventional HE methods, particularly in challenging scenarios like tunnels.
Visual and qualitative evaluations confirmed the method's effectiveness, showing upper-middle-class rankings in image quality and processing speed.

Conclusions:

The developed contrast enhancement technique is effective for LWIR uncooled thermal-imaging cameras in autonomous driving applications.
The method provides significant improvements in image quality essential for reliable perception in autonomous platforms.