Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction
This addresses the problem of real-time, high-resolution spectral imaging for applications like surgery, though it appears incremental as it builds on existing camera technologies.
The paper tackled the limitations of existing spectral imaging systems, such as lack of real-time capability and low spatial resolution, by combining two multispectral snapshot cameras into a stereo system to enable continuous 3D hyperspectral scene reconstruction, with experiments demonstrating feasibility for surgical assistance monitoring.
Spectral imaging enables the analysis of optical material properties that are invisible to the human eye. Different spectral capturing setups, e.g., based on filter-wheel, push-broom, line-scanning, or mosaic cameras, have been introduced in the last years to support a wide range of applications in agriculture, medicine, and industrial surveillance. However, these systems often suffer from different disadvantages, such as lack of real-time capability, limited spectral coverage or low spatial resolution. To address these drawbacks, we present a novel approach combining two calibrated multispectral real-time capable snapshot cameras, covering different spectral ranges, into a stereo-system. Therefore, a hyperspectral data-cube can be continuously captured. The combined use of different multispectral snapshot cameras enables both 3D reconstruction and spectral analysis. Both captured images are demosaicked avoiding spatial resolution loss. We fuse the spectral data from one camera into the other to receive a spatially and spectrally high resolution video stream. Experiments demonstrate the feasibility of this approach and the system is investigated with regard to its applicability for surgical assistance monitoring.