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An Acquisition Method for Visible and Near Infrared Images from Single CMYG Color Filter Array-Based Sensor.

Younghyeon Park¹, Byeungwoo Jeon¹

¹Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea.

Sensors (Basel, Switzerland)

|October 2, 2020

Summary

This summary is machine-generated.

This study introduces a novel single-sensor imaging method for capturing both visible and near-infrared (NIR) images simultaneously. The technique enhances image quality and reduces hardware costs for applications like vein detection and surveillance.

Keywords:

cameras image processing infrared imaging near infrared photography

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Area of Science:

Computer Vision
Image Processing
Optical Engineering

Background:

Near-infrared (NIR) imaging is crucial for applications like banknote recognition, vein detection, and surveillance.
Simultaneous capture of NIR and visible light images is often required.
Existing systems with separate sensors for NIR and visible light are bulky and expensive.

Purpose of the Study:

To develop a cost-effective, single-sensor imaging system for simultaneous visible and NIR image acquisition.
To propose a novel signal separation method for mixed spectral data.
To enhance the quality of separated images through denoising.

Main Methods:

A single-sensor imaging system utilizing a cyan, magenta, yellow, and green color filter array.
Development of a separation matrix derived from mathematical modeling and experimental data.
Application of a denoising process to improve separated image quality.

Main Results:

Successful separation of mixed visible and NIR signals into individual RGB and NIR images.
Achieved average peak-signal-to-noise-ratio (PSNR) of 37.04 dB for RGB and 33.29 dB for NIR images.
Demonstrated superior performance over related work, with PSNR improvements of 6.72 dB (RGB) and 2.55 dB (NIR).

Conclusions:

The proposed single-sensor method effectively separates visible and NIR light signals.
This approach offers a practical and cost-efficient alternative to multi-sensor systems.
The method significantly enhances image quality and separation performance.