Fast Edge-Aware Occlusion Detection in the Context of Multispectral Camera Arrays

TL;DR

This paper introduces a fast, edge-aware occlusion detection method for multispectral camera arrays that significantly reduces processing time and improves registration quality, benefiting applications in healthcare and agriculture.

Contribution

A novel occlusion detection algorithm that is at least 12 times faster and more accurate, enhancing multispectral image registration processes.

Findings

Reduces occlusion detection runtime by at least a factor of 12.

Improves precision and recall in occlusion detection.

Enhances multispectral datacube quality by over 1.5 dB PSNR and SSIM.

Abstract

Multispectral imaging is very beneficial in diverse applications, like healthcare and agriculture, since it can capture absorption bands of molecules in different spectral areas. A promising approach for multispectral snapshot imaging are camera arrays. Image processing is necessary to warp all different views to the same view to retrieve a consistent multispectral datacube. This process is also called multispectral image registration. After a cross spectral disparity estimation, an occlusion detection is required to find the pixels that were not recorded by the peripheral cameras. In this paper, a novel fast edge-aware occlusion detection is presented, which is shown to reduce the runtime by at least a factor of 12. Moreover, an evaluation on ground truth data reveals better performance in terms of precision and recall. Finally, the quality of a final multispectral datacube can be improved by more than 1.5 dB in terms of PSNR as well as in terms of SSIM in an existing multispectral registration pipeline. The source code is available at \url{https://github.com/FAU-LMS/fast-occlusion-detection}.