Digital holography with polarization multiplexing for underwater imaging and descattering

Zhu, Youhua; Yeung, Chok Hang; Lam, Edmund Y.

doi:10.1109/metrosea52177.2021.9611560

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…Compared with a standard off-axis system, the setup can get interference holograms with different polarization states in a single shot, reduces image degradation due to light scatterings, and has higher imaging e ciency. The extra polarization information is capable for the image denoising and more details can be found in [17].…”

Section: The Proposed Methods With An Interferometer and An Ensemble ...mentioning

confidence: 99%

Off-axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification

Lam

Zhu

Buranasiri

2022

Preprint

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This paper proposes a method with an off-axis interferometer and an ensemble deep learning (I-EDL) hologram-classifier to interpret noisy digital holograms captured from the tissues of flawed biological specimens. The holograms are captured by an interferometer which serves as a digital holographic scanner to scan the tissue with 3D information. A red laser beam penetrates the tissue of the specimen. It scans across the x-y directions to capture thousands of off-axis hologram samples for the training, testing, and recognizing the microstructure of the tissue and, hence, identifying the specimens' identity. The method achieves a high success rate of 99.60% in identifying the specimens through the tissue holograms. It is found that the ensemble deep learning hologram-classifier can effectively adapt to optical aberration coming from dust on mirrors and optical lenses aberration such as the Airy-plaque-like rings out-turn from the lenses in the interferometer. The deep learning network effectively adapts to these irregularities during the training stage and performs well in the later recognition stage without prior optical background compensations. The method does not require an intact sample with a full outline shape of the specimens or the organs to understand the objects' identities. It demonstrates a new paradigm in object identification by ensemble deep learning through a direct wavefront recognition technique.

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Section: The Proposed Methods With An Interferometer and An Ensemble ...mentioning

confidence: 99%

Off-axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification

Lam

Zhu

Buranasiri

2022

Preprint

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“…In this way, the adaptive lowlight image enhancement Algortima can improve image quality from various aspects and improve low light quality to the fullest. So this research was made, Adaptive Algorithm to Improve the Image Quality of Vehicle License Plates Based on Lighting Parameters [18,19,20].…”

Section: /314mentioning

confidence: 99%

Adaptive Algorithm to Improve the Image Quality of Vehicle License Plates based on Lighting Parameters

Suhartono,

Suhardi,

Angraini

2023

IOTA

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This study aims to develop an Adaptive Algorithm to Improve the Image Quality of Vehicle Number Plates Based on Lighting Parameters. The type of research used by the Author is Research and Development. This research was conducted at the Computer Engineering Laboratory for six months. This research consists of several stages, from the potential and problem stages, needs analysis, literacy studies, building prototypes, system design, and system testing. The collected datasets were taken using smartphone cameras and webcams, with 207 image datasets divided into two categories: training data and validation. The training dataset of 207 objects was 100% successful. System testing was carried out in two conditions, namely during the day and at night, for each two-wheeled and four-wheeled vehicle object. The results of adaptive algorithm testing to improve the quality of vehicle license plate images based on the light parameter experienced a change in the average MSE and PSNR values between the original image and the quality-improved image, although not too much of a difference. Based on this, it can be interpreted that the adaptive algorithm can produce better photos than before.

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“…Compared with a standard off-axis system, the setup can get raw holograms with different polarization states in a single shot, reduces image degradation due to light scatterings, and has higher imaging efficiency. The extra polarization information is capable of image denoising and more details can be found in [17].…”

Section: Single-shot Off-axis Digital Holographic Interferometermentioning

confidence: 99%

Off-Axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification

2022

Self Cite

View full text Add to dashboard Cite

This paper proposes a method with an off-axis interferometer and an ensemble deep learning (I-EDL) hologram-classifier to interpret noisy digital holograms captured from the tissues of flawed biological specimens. The holograms are captured by an interferometer, which serves as a digital holographic scanner to scan the tissue with 3D information. The method achieves a high success rate of 99.60% in identifying the specimens through the tissue holograms. It is found that the ensemble deep learning hologram-classifier can effectively adapt to optical aberration coming from dust on mirrors and optical lens aberrations such as the Airy-plaque-like rings out-turn from the lenses in the interferometer. The deep learning network effectively adapts to these irregularities during the training stage and performs well in the later recognition stage without prior optical background compensations. The method does not require an intact sample with a full outline shape of the specimens or the organs to understand the objects’ identities. It demonstrates a new paradigm in object identification by ensemble deep learning through a direct wavefront recognition technique.

show abstract

Digital holography with polarization multiplexing for underwater imaging and descattering

Cited by 3 publications

References 28 publications

Off-axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification

Off-axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification

Adaptive Algorithm to Improve the Image Quality of Vehicle License Plates based on Lighting Parameters

Off-Axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification

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