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- Title
Single-Pixel Hyperspectral Imaging via an Untrained Convolutional Neural Network.
- Authors
Wang, Chen-Hui; Li, Hong-Ze; Bie, Shu-Hang; Lv, Rui-Bing; Chen, Xi-Hao
- Abstract
Single-pixel hyperspectral imaging (HSI) has received a lot of attention in recent years due to its advantages of high sensitivity, wide spectral ranges, low cost, and small sizes. In this article, we perform a single-pixel HSI experiment based on an untrained convolutional neural network (CNN) at an ultralow sampling rate, where the high-quality retrieved images of the target objects can be achieved by every visible wavelength of a light source from 432 nm to 680 nm. Specifically, we integrate the imaging physical model of single-pixel HSI into a randomly initialized CNN, which allows the images to be reconstructed by relying solely on the interaction between the imaging physical process and the neural network without pre-training the neural network.
- Subjects
CONVOLUTIONAL neural networks; PIXELS; LIGHT sources; VISIBLE spectra; HABITAT suitability index models
- Publication
Photonics, 2023, Vol 10, Issue 2, p224
- ISSN
2304-6732
- Publication type
Article
- DOI
10.3390/photonics10020224