HoloDiffusion: Sparse Digital Holographic Reconstruction via Diffusion Modeling

In digital holography, reconstructed image quality can be primarily limited due to the inability of a single small aperture sensor to cover the entire field of a hologram. The use of multi-sensor arrays in synthetic aperture digital holographic imaging technology contributes to overcoming the limita...

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Bibliographic Details
Published in:Photonics Vol. 11; no. 4; p. 388
Main Authors: Zhang, Liu, Gao, Songyang, Tong, Minghao, Huang, Yicheng, Zhang, Zibang, Wan, Wenbo, Liu, Qiegen
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2024
Subjects:
Accuracy
Aperture
Arrays
Diffusion
diffusion modeling
digital holography
Digital imaging
Holography
Image processing
Image quality
Image reconstruction
Imaging systems
Neural networks
Sensor arrays
Sensors
Signal to noise ratio
sparse sensor array sampling
Synthetic apertures
ISSN:2304-6732, 2304-6732
Online Access:Get full text
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Summary:In digital holography, reconstructed image quality can be primarily limited due to the inability of a single small aperture sensor to cover the entire field of a hologram. The use of multi-sensor arrays in synthetic aperture digital holographic imaging technology contributes to overcoming the limitations of sensor coverage by expanding the area for detection. However, imaging accuracy is affected by the gap size between sensors and the resolution of sensors, especially when dealing with a limited number of sensors. An image reconstruction method is proposed that combines physical constraint characteristics of the imaging object with a score-based diffusion model, aiming to enhance the imaging accuracy of digital holography technology with extremely sparse sensor arrays. Prior information of the sample is learned by the neural network in the diffusion model to obtain a score function, which alternately constrains the iterative reconstruction process with the underlying physical model. The results demonstrate that the structural similarity and peak signal-to-noise ratio of the reconstructed images using this method are higher than the traditional method, along with a strong generalization ability.
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ISSN:2304-6732
2304-6732
DOI:10.3390/photonics11040388