Simultaneous depth determination of multiple objects by focus analysis in digital holography

Tachiki, Mark L.; Itoh, Masahide; Yatagai, Toyohiko

doi:10.1364/ao.47.00d144

Cited by 54 publications

(26 citation statements)

References 16 publications

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“…As a result an image with all parts in focus and a topology map can be obtained. The EDOF method was already applied to digital holography in [18][19][20], but to the authors knowledge it is the first time to report the combination of synthetic aperture method with the EDOF method. Not all algorithms used in microscopy to define the in-focus object regions can successfully be applied in digital holography due to the speckle effect.…”

Section: Extended Depth Of Fieldmentioning

confidence: 99%

“…Not all algorithms used in microscopy to define the in-focus object regions can successfully be applied in digital holography due to the speckle effect. The most promising algorithm is based on the variance approach, demonstrated in [19,20], which was also applied in this paper. Speckle noise apparent in the numerical reconstruction produces a large change of variance.…”

Section: Extended Depth Of Fieldmentioning

confidence: 99%

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High resolution digital holographic synthetic aperture applied to deformation measurement and extended depth of field method

Claus

2010

Appl. Opt.

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Section: Extended Depth Of Fieldmentioning

confidence: 99%

Section: Extended Depth Of Fieldmentioning

confidence: 99%

High resolution digital holographic synthetic aperture applied to deformation measurement and extended depth of field method

Claus

2010

Appl. Opt.

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“…Compare this against the gradient: while the gradient extrema is centered above the edge, the gradient magnitude goes linearly as k=2: The quadratic factor gives the maximum Laplacian magnitude metric greater depth discriminability than the maximum gradient magnitude as the edge comes into focus. It should be noted that Tachiki also uses the magnitude of the Laplacian as a focal metric, integrated over a local neighborhood [369] -with the spatial integration removing a degree of the discriminative bene…t of the Laplacian.…”

Section: Domínguez-caballero Deviated From the Norm And Searched Instmentioning

confidence: 99%

“…It has seen use in the past for shape-from-focus techniques using ad hoc arguments for its e¤ectiveness [235], [369], [248]. The local variance is small when the intensity is nearly uniform, and grows as the intensity range increases.…”

Section: Domínguez-caballero Deviated From the Norm And Searched Instmentioning

confidence: 99%

Computational imaging and automated identification for aqueous environments

Loomis¹

2011

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Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi…cation with bag-of-words models and multi-stage boosting for rock…sh detection. Methods for extracting images of …sh from videos of longline operations are demonstrated.A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the e¤ects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references.Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the e¢ cacy of the prototype and algorithms. Course work at MIT and WHOI was helpful throughout this thesis. In particular, course materials and projects from Dr. Antonio Torralba (computer vision, detection, and boosting), Dr. Frédo Durand (computational imaging, segmentation, and probability), Dr. Rosalind Picard (machine learning, pattern recognition, and probability), Dr. Alan 4 Edelman (parallel computing and algorithms), and Dr. Barbastathis (optical systems) are re ‡ected directly, in several cases extended after the course to become complete sections of this thesis.The MIT Museum has provided unique outreach opportunities for the ideas presented in this thesis. Seth Riskin originally involved our lab in holography activities at the MIT Museum, then Kurt Hasselbalch later proposed and guided the development of an interactive display on plankton holography. The computational approaches I learned for the museum display led to further scienti…c development and spurred the use of GPUs for processing; Section 3.3.5 is a direct consequence, as is the majority of the data processing performed throughout this thesis.In reference to data processing, Matlab/MEX and NVIDIA's CUDA have been daily cornerstones for which I am continually thankful. Finally, mad props go to Dr. José "Pepe" Domínguez-Caballero, the most in ‡uential 5 person in my scienti…c development while at MIT. Pepe taught me digital holography and worked one-on-one with me throughout the beginning of my grad student career. He continued to be involved as a fellow holographer, a mentor, and a friend, discussing ideas, encouraging active experimentation, maintaining a curiosity about optics, and o¤ering eno...

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“…This is fulfilled by the layer by layer reconstruction of the measured hologram, and finding the extreme values of an applied proper focus measure within this reconstructed volume [9]. The thickness of the reconstructed layers is defined by the depth of field of the optical system.…”

Section: Algorithmmentioning

confidence: 99%

GPU implementation of volume reconstruction and object detection in Digital Holographic Microscopy

Orzó

Gӧrӧcs

Szatmári

et al. 2010

2010 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA 2010)

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Using Digital Holographic Microscopy (DHM) we can gather information from a whole volume and thus we can avoid the small depth of field constraint of the conventional microscopes. This way a volume inspection system can be constructed, which is capable to find, segment, collect, and later classify those objects that flow through an inspection chamber. Digital hologram reconstruction and processing, however, require considerable computational resources. We are developing volume reconstruction and object detection algorithms that can speed up considerably by parallel hardware implementation. Therefore, we put these tasks into operation on a GPU. As data transfer of the reconstructed planes would slow down the algorithm, all the reconstruction, object detection processes are to be completed on the parallel hardware, while fine tuning of object reconstruction and classification will be done on a CPU later. The actual speed up of the GPU implemented algorithm comparing to its conventional CPU realization depends on the applied hardware devices. So far we reached a 10 times acceleration value.

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Simultaneous depth determination of multiple objects by focus analysis in digital holography

Cited by 54 publications

References 16 publications

High resolution digital holographic synthetic aperture applied to deformation measurement and extended depth of field method

High resolution digital holographic synthetic aperture applied to deformation measurement and extended depth of field method

Computational imaging and automated identification for aqueous environments

GPU implementation of volume reconstruction and object detection in Digital Holographic Microscopy

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