Mid-infrared Incoherent Three-Dimensional Imaging Using Lucy-Richardson-Rosen Algorithm

Anand, Vijayakumar; Han, Molong; Maksimovic, Jovan; Ng, Soon Hock; Katkus, Tomas; Klein, Annaleise R.; Bambery, Keith R.; Vongsvivut, Jitraporn; Juodkazis, Saulius

doi:10.1364/3d.2022.jw5b.5

Cited by 19 publications

(30 citation statements)

References 12 publications

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“…LR 2 A is a recently developed computational reconstruction method that integrates two well-known algorithms, namely LRA and NLR [16]. In this study, LR 2 A was implemented for the deblurring of images captured by a monochrome camera in the presence of spatial and spectral aberrations and with a limited numerical aperture.…”

Section: Discussionmentioning

confidence: 99%

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Lucy-Richardson-Rosen Algorithm Assisted Classification of Blurred Images with Deep Learning Networks

Amudhavel¹,

Gopinath²,

Praveen³

et al. 2023

Preprint

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Pattern recognition techniques form the heart of most, if not all, incoherent linear shift-invariant systems. When an object is recorded using a camera, the object information gets sampled by the point spread function (PSF) of the system, replacing every object point with the PSF in the sensor. The PSF is a sharp Kronecker Delta-like function when the numerical aperture (NA) is large with no aberrations. When the NA is small, and the system has aberrations, the PSF appears blurred. In the above case, if the PSF is known, then the object information can be obtained by scanning the PSF over the recorded object intensity pattern and looking for pattern matching conditions through a mathematical process called correlation. In this study, a recently developed deconvolution method, the Lucy-Richardson-Rosen algorithm (LR2A), has been implemented to computationally refocus images recorded in the presence of spatio-spectral aberrations. The performance of LR2A was compared against the Lucy-Richardson algorithm and non-linear reconstruction. LR2A exhibits a superior deconvolution capability even in extreme cases of spatio-spectral aberrations and blur. Experimental results of deblurring a picture captured using high-resolution smartphone cameras are presented. LR2A was implemented to significantly improve the performances of the widely used deep convolutional neural networks for image classification.

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Section: Discussionmentioning

confidence: 99%

“…However, LRA has only a limited application range and cannot be implemented for a wide range of optical modulators such as NLR. Recently, a new deconvolution method LR 2 A was developed by replacing the correlation in LRA by NLR [16]. The schematic of the reconstruction method is shown in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

Lucy-Richardson-Rosen Algorithm Assisted Classification of Blurred Images with Deep Learning Networks

Amudhavel¹,

Gopinath²,

Praveen³

et al. 2023

Preprint

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“…While most of the commercial FTIR systems are limited to measuring the absorption spectrum of a sample, the infrared microscope (IRM) of the Australian Synchrotron beamline is capable of measuring both the absorption spectrum as well as spectral images [2]. The above capability at the Australian Synchrotron IRM beamline is useful for many advanced studies [3][4][5][6]. One of the challenges in imaging in IR region is the lack of high-resolution detectors.…”

Section: Introductionmentioning

confidence: 99%

“…The current state-of-the-art measurement allows only recording the 2D spectral images. Recently, 3D imaging capability has been introduced in the IRM system using computational imaging methods [5,6]. In studying biochemical samples, if the refractive index variation across the sample can be obtained in addition to the intensity and spectrum, a better understanding of the sample may be possible.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional phase imaging with near infrared synchrotron beam using phase-retrieval algorithm

Han

Smith

et al. 2023

Practical Holography XXXVII: Displays, Materials, and Applications

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The near infrared (NIR) part of the infrared synchrotron beam is usually dumped to improve the signal to noise ratio of spectral imaging. In this study, this NIR synchrotron beam has been extracted and used for three-dimensional (3D) phase imaging. A pinhole was inserted in the path of the fork shaped NIR synchrotron beam and the Airy diffraction pattern was aligned with biochemical samples and the diffracted intensity distribution was captured using an image sensor sensitive to NIR. A phase retrieval algorithm was used to estimate the 3D phase distribution at the object plane from the recorded intensity distribution.

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“…But in this case, the high imaging resolution is obtained at the expense of low temporal resolution. In the recent years, there have been many advanced studies carried out at the IRM of the Australian synchrotron [3][4][5][6]. In [5,6], novel computational imaging techniques have been implemented at the IRM system to achieve 3D imaging of samples from a single camera shot.…”

Section: Introductionmentioning

confidence: 99%

Computational three-dimensional Imaging with near infrared synchrotron beam using Fresnel zone apertures fabricated on barium fluoride windows using femtosecond laser ablation

Smith¹,

Han²,

Ng³

et al. 2023

Practical Holography XXXVII: Displays, Materials, and Applications

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The near infrared (NIR) part of the infrared synchrotron beam is usually discarded to improve the signal to noise ratio of spectral imaging at the Australian Synchrotron. In this study, NIR synchrotron beam has been extracted and used for three-dimensional (3D) imaging. A Fresnel zone aperture (FZA) was fabricated on barium fluoride windows using femtosecond ablation. The 3D point spread functions (PSFs) were recorded using the FZA mounted between the pinhole and the image sensor. An object is then placed within the boundaries of the PSF library and an object intensity distribution was recorded. Computational reconstruction methods were applied to reconstruct the object information.

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Mid-infrared Incoherent Three-Dimensional Imaging Using Lucy-Richardson-Rosen Algorithm

Abstract: Two computational reconstruction methods namely the Lucy-Richardson algorithm and non-linear reconstruction have been combined to develop Lucy-Richardson-Rosen algorithm. This new algorithm has been used to convert a two-dimensional infrared spectral map into a three-dimensional image.

Cited by 19 publications

References 12 publications

Lucy-Richardson-Rosen Algorithm Assisted Classification of Blurred Images with Deep Learning Networks

Lucy-Richardson-Rosen Algorithm Assisted Classification of Blurred Images with Deep Learning Networks

Three-dimensional phase imaging with near infrared synchrotron beam using phase-retrieval algorithm

Computational three-dimensional Imaging with near infrared synchrotron beam using Fresnel zone apertures fabricated on barium fluoride windows using femtosecond laser ablation

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