Computational ghost imaging enhanced by degradation models for under-sampling.
Opt Lett
; 49(18): 5296-5299, 2024 Sep 15.
Article
en En
| MEDLINE
| ID: mdl-39270289
ABSTRACT
Computational ghost imaging (CGI) allows two-dimensional (2D) imaging by using spatial light modulators and bucket detectors. However, most CGI methods attempt to obtain 2D images through measurements with a single sampling ratio. Here, we propose a CGI method enhanced by degradation models for under-sampling, which can be reflected by results from measurements with different sampling ratios. We utilize results from low-sampling-ratio measurements and normal-sampling-ratio measurements to train the neural network for the degradation model, which is fitted through self-supervised learning. We obtain final results by importing normal-sampling-ratio results into the neural network with optimal parameters. We experimentally demonstrate improved results from the CGI method using degradation models for under-sampling. Our proposed method would promote the development of CGI in many applications.
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MEDLINE
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En
Revista:
Opt Lett
Año:
2024
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Article