Dual camera snapshot hyperspectral imaging system via physics-informed learning
FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV)
Image and Video Processing (eess.IV)
Computer Science - Computer Vision and Pattern Recognition
FOS: Electrical engineering, electronic engineering, information engineering
0202 electrical engineering, electronic engineering, information engineering
FOS: Physical sciences
02 engineering and technology
Electrical Engineering and Systems Science - Image and Video Processing
Physics - Optics
Optics (physics.optics)
DOI:
10.1016/j.optlaseng.2022.107023
Publication Date:
2022-03-20T09:50:40Z
AUTHORS (6)
ABSTRACT
We consider using the system's optical imaging process with convolutional neural networks (CNNs) to solve the snapshot hyperspectral imaging reconstruction problem, which uses a dual-camera system to capture the three-dimensional hyperspectral images (HSIs) in a compressed way. Various methods using CNNs have been developed in recent years to reconstruct HSIs, but most of the supervised deep learning methods aimed to fit a brute-force mapping relationship between the captured compressed image and standard HSIs. Thus, the learned mapping would be invalid when the observation data deviate from the training data. Especially, we usually don't have ground truth in real-life scenarios. In this paper, we present a self-supervised dual-camera equipment with an untrained physics-informed CNNs framework. Extensive simulation and experimental results show that our method without training can be adapted to a wide imaging environment with good performance. Furthermore, compared with the training-based methods, our system can be constantly fine-tuned and self-improved in real-life scenarios.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (35)
CITATIONS (14)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....