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Attention-enhanced computational ghost imaging
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AI
DOI：10.1007/s11432-024-4434-5.png)
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Abstract
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In this study, we propose an attention-enhanced computational ghost imaging method (AEGI). AEGI integrates the attention mechanism into the framework of computational ghost imaging. In the process of image reconstruction, the attention mechanism identifies and captures object-relevant information from the extracted features, constrained by the discrepancy between the 1D intensity of the predicted and actual object image. This process can adjust the weight of the feature by exploring the relationship between the feature and adjacent features, thus enhancing the object signal while suppressing background noise in the final image. In addition, we have conducted some experiments in unfamiliar space and underwater environments to verify the effectiveness of AEGI. The results show that AEGI can reconstruct object images with high quality, which greatly enhances the practical application capabilities of computational ghost imaging.
Keywords:
computational ghost imaging
attention mechanism
deep learning
self-supervised
speckle pattern
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