Computational Ghost Imaging Base on Bidirectional Recurrent Neural Network
Article No.: 51, Pages 1 - 5
Abstract
In recent decades, Ghost Imaging (GI) has emerged as a focal point of research attention among scholars. The imaging quality of GI remains a challenge despite extensive research. In this study, we propose a new model aimed at improving imaging quality. Being inspired by the possible nonlinear connections in GI, we have developed a model based on bidirectional recurrent neural networks (BI-RNN), named GI-BRNN(ghost imaging base on bidirectional recurrent neural network). This model leverages the bidirectional recurrent mechanism and exhibits higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to traditional algorithms. The results of this study demonstrate that the GI-BRNN model significantly improves the quality of ghost imaging. This research provides new insights and approaches for the application of deep learning in the field of GI.
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Published In
April 2024
381 pages
ISBN:9798400716393
DOI:10.1145/3661725
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Association for Computing Machinery
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Publication History
Published: 20 June 2024
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CMLDS 2024
CMLDS 2024: 2024 International Conference on Computing, Machine Learning and Data Science
April 12 - 14, 2024
Singapore, Singapore
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