Inter-Camera Color Correction for Multispectral Imaging with Camera Arrays Using a Consensus Image

TL;DR

This paper presents a new inter-camera color calibration method for multispectral camera arrays that uses a consensus image derived from statistical information, improving calibration accuracy over traditional single-reference approaches.

Contribution

The proposed method introduces a consensus image approach for calibration, leveraging statistical data to enhance multispectral camera array color correction.

Findings

Improved PSNR by 1.15 dB for linear regression correction.

Enhanced color difference (iCID) by 2.81.

Better calibration accuracy compared to existing methods.

Abstract

This paper introduces a novel method for inter-camera color calibration for multispectral imaging with camera arrays using a consensus image. Capturing images using multispectral camera arrays has gained importance in medical, agricultural, and environmental processes. Due to fabrication differences, noise, or device altering, varying pixel sensitivities occur, influencing classification processes. Therefore, color calibration between the cameras is necessary. In existing methods, one of the camera images is chosen and considered as a reference, ignoring the color information of all other recordings. Our new approach does not just take one image as reference, but uses statistical information such as the location parameter to generate a consensus image as basis for calibration. This way, we managed to improve the PSNR values for the linear regression color correction algorithm by 1.15 dB and the improved color difference (iCID) values by 2.81.