50 research outputs found
Comparative analysis and implementation of structured edge active contour
This paper proposes modified chanvese model which can be implemented on image for segmentation. The structure of paper is based on Linear structure tensor (LST) as input to the variant model. Structure tensor is a matrix illustration of partial derivative information. In the proposed model, the original image is considered as information channel for computing structure tensor. Difference of Gaussian (DOG) is featuring improvement in which we can get less blurred image than original image.In this paper LST is modified by adding intensity information to enhance orientation information. Finally Active Contour Model (ACM) is used to segment the images. The proposed algorithm is tested on various images and also on some images which have intensity inhomogeneity and results are shown. Also, the results with other algorithms like chanvese, Bhattacharya, Gabor based chanvese and Novel structure tensor based model are compared.It is verified that accuracy of proposed model is the best. The biggest advantage of proposed model is clear edge enhancement
CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training
Recent image inpainting methods have made great progress but often struggle
to generate plausible image structures when dealing with large holes in complex
images. This is partially due to the lack of effective network structures that
can capture both the long-range dependency and high-level semantics of an
image. To address these problems, we propose cascaded modulation GAN (CM-GAN),
a new network design consisting of an encoder with Fourier convolution blocks
that extract multi-scale feature representations from the input image with
holes and a StyleGAN-like decoder with a novel cascaded global-spatial
modulation block at each scale level. In each decoder block, global modulation
is first applied to perform coarse semantic-aware structure synthesis, then
spatial modulation is applied on the output of global modulation to further
adjust the feature map in a spatially adaptive fashion. In addition, we design
an object-aware training scheme to prevent the network from hallucinating new
objects inside holes, fulfilling the needs of object removal tasks in
real-world scenarios. Extensive experiments are conducted to show that our
method significantly outperforms existing methods in both quantitative and
qualitative evaluation.Comment: 32 pages, 18 figure
Von Pixeln zu Regionen: Partielle Differentialgleichungen in der Bildanalyse
This work deals with applications of partial differential equations in image analysis. The focus is thereby on applications that can be used for image segmentation. This includes, among other topics, nonlinear diffusion, motion analysis, and image segmentation itself. From each chapter to the next, the methods are directed more and more to image segmentation. While Chapter 2 presents general denoising and simplification techniques, Chapter 4 already addresses the somewhat more special task to extract texture and motion from images. This is in order to employ the resulting features to the partitioning of images finally in Chapter 5. Thus, in this work, one can clearly make out the thread from the raw image data, the pixels, to the more abstract descriptions of images by means of regions. The fact that image processing techniques can also be useful in research areas besides conventional images is shown in Chapter 3. They are used here in order to improve numerical methods for conservation laws in physics. The work conceptually focuses on using as many different features as possible for segmentation. This includes besides image-driven features like texture and motion the knowledge-based information of a three-dimensional object model. The basic idea of this concept is to provide a preferably wide basis of information for separating object regions and thus increasing the number of situations in which the method yields satisfactory segmentation results.
A further basic concept pursued in this thesis is to employ coarse-to-fine strategies. They are used both for motion estimation in Chapter 4 and for segmentation in Chapter 5. In both cases one has to deal with optimization problems that contain many local optima. Conventional local optimization therefore usually leads to results the quality of which heavily depends
on the initialization. This situation can often be eased, if the optimization problem is first significantly simplified. One then tries to solve the original problem by continuously increasing the problem complexity.
Apart from this, the work contains several essential technical novelties. In Chapter 2, nonlinear diffusion with unbounded diffusivities is considered. This also includes total variation flow(TV flow). A thorough analysis of TV flow thereby leads to an analytic solution that allows to show that TV flow is in the space-discrete, one-dimensional setting exactly identical to the corresponding variational approach called TV regularization. Moreover, various different numerical methods are investigated in order to determine their suitability for diffusion filters with unbounded diffusivities. TV flow can be regarded as an alternative to Gaussian smoothing, though there is the significant difference of TV flow being discontinuity preserving. By replacing Gaussian smoothing by TV flow, one can develop new discontinuity preserving versions of well-known operators such as the structure tensor. TV flow is also employed in Chapter 3 where the goal is to improve numerical schemes for the approximation of hyperbolic conservation laws by means of image processing techniques.
The role of TV flow in this scope is to remove oscillations of a second order method. In an alternative approach, the approximation performance of a first order method is improved by a nonlinear inverse diffusion filter. The underlying concept is to remove exactly the amount of numerical diffusion that actually stabilizes the scheme. By means of an appropriate stabilization of the inverse diffusion process it is possible to preserve the positive stability properties
of the original method.
III
IV Abstract
Chapter 4 is separated into two parts. The first part deals with the extraction of texture features, whereas the second part focuses on motion estimation. Goal of the texture extraction method is to derive a feature space that is as low-dimensional as possible but still provides very good discrimination properties. The basic framework of this feature space is the structure tensor based on TV flow presented earlier in Chapter 2. It contains the orientation, magnitude, and homogeneity of a texture and therefore provides already very important features for texture discrimination. Additionally, a region based local scale measure is developed that supplements the size of texture elements to the feature space. This feature space is used later
in Chapter 5 for texture segmentation. Two motion estimation methods are introduced in Chapter 4. One of them is based on the structure tensor from Section 2 and improves existing local methods. The other technique
is based on a global variational approach. It differs from usual variational approaches by the use of a gradient constancy assumption. This assumption provides the method with the capability to yield good estimation results even in the presence of small local or global variations of illumination. Besides this novelty, the combination of non-linearized constancy assumptions and a coarse-to-fine strategy yields a numerical scheme that provides for the first
time a well founded theory for the very successful warping methods. The described technique leads to results that are generally more accurate than all results presented in literature so far. As already mentioned, goal of the image segmentation approach in Chapter 5 is mainly to integrate the features derived in Chapter 4 and to utilize a coarse-to-fine strategy. This is done in the framework of region based, implicit active contour models which are set up on
the concept of level sets. The involved region models are extended by nonparametric as well as local region statistics.
A further novelty is the extension of the level set concept to multiple regions. The optimum number of regions is thereby estimated by a hierarchical approach. This is a considerable extension of conventional active contour models, which are usually restricted to two regions. Moreover, the idea to use three-dimensional object knowledge for segmentation is presented. The proposed method uses the extracted contour for estimating the pose of the object, while
in return the projected object model supports the segmentation. The implementation of this idea as described in this thesis is only at an early stage. Plenty of interesting aspects can be derived from this concept that are to be investigated in the future.Die vorliegenden Arbeit beschĂ€ftigt sich mit Anwendungen partieller Differentialgleichungen in der Bildanalyse. Dabei stehen Anwendungen im Vordergrund, die sich zur Bildsegmentierung verwenden lassen. Dies schlieĂt unter anderem nichtlineare Diffusion, BewegungsschĂ€tzung und die Bildsegmentierung selbst ein. Von Kapitel zu Kapitel werden die verwendeten Methoden dabei mehr und mehr auf die Bildsegmentierung ausgerichtet. Werden in Kapitel 2 noch allgemeine Entrauschungs- und Bildvereinfachungsoperationen vorgestellt, behandelt Kapitel 4 die schon etwas speziellere Aufgabe, Textur und Bewegung aus Bildern zu extrahieren, um entsprechende Merkmale schlieĂlich in Kapitel 5 zur Segmentierung von Bildern verwenden zu können. Dabei zieht sich der Weg von den rohen Bilddaten, den Pixeln, hin zur abstrakteren Beschreibung von Bildern mit Hilfe von Regionen als roter Faden durch die gesamte Arbeit. Dass sich Bildverarbeitungstechniken auch in Forschungsgebieten fern herkömmlicher Bilder als nĂŒtzlich erweisen können, zeigt Kapitel 3. Hier werden Bildverarbeitungstechniken zur Verbesserung numerischer Verfahren fĂŒr Erhaltungsgleichungen der Physik verwendet. Konzeptionell legt diese Arbeit Wert darauf, möglichst viele verschiedene Merkmale zur Segmentierung zu verwenden. Darunter fallen neben den bildgestĂŒtzten Merkmalen wie Textur und Bewegung auch die wissensbasierte Information eines dreidimensionalen OberflĂ€chenmodells. Die prinzipielle Idee hinter diesem Konzept ist, die Entscheidungsgrundlage zur Trennung von Objektregionen auf eine möglichst breite Informationsbasis zu stellen und somit die Anzahl der Situationen, in denen das Verfahren zufriedenstellende Segmentierungsergebnisse liefert, zu erhöhen. Ein weiteres Grundkonzept, das in dieser Arbeit verfolgt wird, ist die Verwendung von Coarse- To-Fine-Strategien. Sie kommen sowohl bei der BewegungsschĂ€tzung in Kapitel 4 als auch in der Segmentierung in Kapitel 5 zum Einsatz. In beiden FĂ€llen hat man es mit Optimierungsproblemen zu tun, die viele lokale Optima aufweisen. Herkömmliche lokale Optimierung fĂŒhrt daher meist zu Ergebnissen, deren QualitĂ€t stark von der Initialisierung abhĂ€ngt. Diese Situation lĂ€sst sich hĂ€ufig entschĂ€rfen, wenn man das entsprechende Optimierungsproblem zunĂ€chst deutlich vereinfacht und erst nach und nach das ursprĂŒngliche Problem zu lösen versucht. Daneben enthĂ€lt diese Arbeit viele wesentliche technische Neuerungen. In Kapitel 2 wird nichtlineare Diffusion mit unbeschrĂ€nkten DiffusivitĂ€ten betrachtet, was auch Total-Variation- Flow (TV-Flow) mit einschlieĂt. Eine genaue Analyse von TV-Flow fĂŒhrt dabei zu einer analytischen Lösung, mit Hilfe derer man zeigen kann, dass TV-Flow im diskreten, eindimensionalen Fall exakt identisch mit dem ensprechenden Variationsansatz der TV-Regularisierung ist. Desweiteren werden verschiedene numerische Verfahren in Bezug auf ihre Eignung fĂŒr Diffusionsfilter mit unbeschrĂ€nkten DiffusivitĂ€ten untersucht. Man kann TV-Flow als eine Alternative zur GauĂglĂ€ttung ansehen, mit dem entscheidenden Unterschied, dass TV-Flow kantenerhaltend ist. Durch Ersetzen von GauĂglĂ€ttung durch TV-Flow lassen sich so diskontinuitĂ€tserhaltende Varianten bekannter Operatoren wie etwa des Strukturtensors entwickeln. Auch in Kapitel 3 kommt TV-Flow zum Einsatz, wenn es darum geht, numerische Verfahren zur Approximation hyperbolischer Erhaltungsgleichungen durch Bildverarbeitungsmethoden zu verbessern. TV-Flow fĂ€llt dabei die Rolle zu, Oszillationen eines Verfahrens zweiter Ordnung zu beseitigen. In einem alternativen Ansatz werden die Approximationseigenschaften eines Verfahrens erster Ordnung durch einen nichtlinearen RĂŒckwĂ€rtsdiffusionsfilter verbessert, indem die numerische Diffusion, die das Verfahren eigentlich stabilisiert, gezielt wieder entfernt wird. Dabei gelingt es durch eine geeignete Stabilisierung der RĂŒckwĂ€rtsdiffusion, die positiven StabilitĂ€tseigenschaften des Originalverfahrens zu erhalten. Kapitel 4 spaltet sich in zwei Teile auf, wobei der erste Teil von der Extrahierung von Texturmerkmalen handelt, wĂ€hrend sich der zweite Teil auf BewegungsschĂ€tzung konzentriert. Bei den Texturmerkmalen besteht dabei das Ziel, einen möglichst niederdimensionalen Merkmalsraum zu kreieren, der dennoch sehr gute Diskriminierungseigenschaften besitzt. Das GrundgerĂŒst dieses Merkmalsraums stellt dabei der in Kapitel 2 vorgestellte, auf TV-Flow basierende Strukturtensor dar. Er beschreibt mit der Orientierung, StĂ€rke und HomogenitĂ€t der Texturierung bereits sehr wichtige Merkmale einer Textur. Daneben wird ein regionenbasiertes, lokales SkalenmaĂ entwickelt, das zusĂ€tzlich die GröĂe von Texturelementen als Merkmal einbringt. Diese Texturmerkmale werden spĂ€ter in Kapitel 5 zur Textursegmentierung verwendet. Zur BewegungsschĂ€tzung werden zwei Verfahren vorgestellt. Das eine basiert auf dem in Kapitel 2 eingefĂŒhrten Strukturtensor und stellt eine Verbesserung vorhandener lokaler Methoden dar. Das andere Verfahren basiert auf einem globalen Variationsansatz und unterscheidet sich von ĂŒblichen VariationsansĂ€tzen durch die Verwendung einer Gradientenkonstanzannahme. Diese stattet das Verfahren mit der FĂ€higkeit aus, auch beim Vorhandensein kleinerer lokaler oder globaler Helligkeitsschwankungen gute SchĂ€tzergebnisse zu liefern. Daneben ergibt sich aus der Kombination von nicht-linearisierten Konstanzannahmen und einer Coarse-To-Fine-Strategie ein numerisches Schema, das erstmals eine fundierte Theorie zu den sehr erfolgreichen Warping-Verfahren zur VerfĂŒgung stellt. Mit der beschriebenen Technik werden Ergebnisse erzielt, die grundsĂ€tzlich prĂ€ziser sind als alles was bisher in der Literatur vorgestellt wurde. Bei der eigentlichen Bildsegmentierung in Kapitel 5 geht es schlieĂlich, wie bereits erwĂ€hnt, hauptsĂ€chlich um die Einbringung der in Kapitel 4 entwickelten zusĂ€tzlichen Merkmale und um die Verwendung einer Coarse-To-Fine-Strategie. Dies geschieht im Rahmen von regionenbasierten, impliziten Aktiv-Kontur-Modellen, die auf dem Konzept der Level-Sets aufbauen. Dabei werden die Regionenmodelle um nichtparametrische und lokale Beschreibungen der Regionenstatistik erweitert. Eine weitere Neuerung ist die Erweiterung des Level-Set-Konzepts auf mehrere Regionen. In einem teils hierarchischen Ansatz wird dabei auch die optimale Anzahl der Regionen geschĂ€tzt, was eine erhebliche Erweiterung im Vergleich zu herkömmlichen Aktiv-Kontur- Modellen darstellt. AuĂerdem wird die Idee vorgestellt, dreidimensionales Objektwissen in der Segmentierung zu verwenden, indem anhand der Segmentierung die Lage des Objekts geschĂ€tzt wird und umgekehrt wiederum das projizierte Objektmodell die Segmentierung unterstĂŒtzt. Die Umsetzung dieser Idee, wie sie in dieser Arbeit beschrieben wird, steht dabei erst am Anfang. FĂŒr die Zukunft ergeben sich hieraus noch viele interessanter Aspekte, die es zu untersuchen gilt
Deformable models for adaptive radiotherapy planning
Radiotherapy is the most widely used treatment for cancer, with 4 out of 10 cancer patients
receiving radiotherapy as part of their treatment. The delineation of gross tumour volume
(GTV) is crucial in the treatment of radiotherapy. An automatic contouring system would be
beneficial in radiotherapy planning in order to generate objective, accurate and reproducible
GTV contours. Image guided radiotherapy (IGRT) acquires patient images just before treatment
delivery to allow any necessary positional correction. Consequently, real-time contouring
system provides an opportunity to adopt radiotherapy on the treatment day. In this thesis, freely
deformable models (FDM) and shape constrained deformable models (SCDMs) were used to
automatically delineate the GTV for brain cancer and prostate cancer.
Level set method (LSM) is a typical FDM which was used to contour glioma on brain MRI. A
series of low level image segmentation methodologies are cascaded to form a case-wise fully
automatic initialisation pipeline for the level set function. Dice similarity coefficients (DSCs)
were used to evaluate the contours. Results shown a good agreement between clinical contours
and LSM contours, in 93% of cases the DSCs was found to be between 60% and 80%.
The second significant contribution is a novel development to the active shape model (ASM), a
profile feature was selected from pre-computed texture features by minimising the Mahalanobis
distance (MD) to obtain the most distinct feature for each landmark, instead of conventional
image intensity. A new group-wise registration scheme was applied to solve the correspondence
definition within the training data. This ASM model was used to delineated prostate GTV on
CT. DSCs for this case was found between 0.75 and 0.91 with the mean DSC 0.81.
The last contribution is a fully automatic active appearance model (AAM) which captures
image appearance near the GTV boundary. The image appearance of inner GTV was discarded
to spare the potential disruption caused by brachytherapy seeds or gold markers. This model
outperforms conventional AAM at the prostate base and apex region by involving surround
organs. The overall mean DSC for this case is 0.85
Beyond imaging with coherent anti-Stokes Raman scattering microscopy
La microscopie optique permet de visualiser des Ă©chantillons biologiques avec une bonne sensibilitĂ© et une rĂ©solution spatiale Ă©levĂ©e tout en interfĂ©rant peu avec les Ă©chantillons. La microscopie par diffusion Raman cohĂ©rente (CARS) est une technique de microscopie non linĂ©aire basĂ©e sur lâeffet Raman qui a comme avantage de fournir un mĂ©canisme de contraste endogĂšne sensible aux vibrations molĂ©culaires. La microscopie CARS est maintenant une modalitĂ© dâimagerie reconnue, en particulier pour les expĂ©riences in vivo, car elle Ă©limine la nĂ©cessitĂ© dâutiliser des agents de contraste exogĂšnes, et donc les problĂšmes liĂ©s Ă leur distribution, spĂ©cificitĂ© et caractĂšre invasif. Cependant, il existe encore plusieurs obstacles Ă lâadoption Ă grande Ă©chelle de la microscopie CARS en biologie et en mĂ©decine : le coĂ»t et la complexitĂ© des systĂšmes actuels, les difficultĂ©s dâutilisation et dâentretient, la rigiditĂ© du mĂ©canisme de contraste, la vitesse de syntonisation limitĂ©e et le faible nombre de mĂ©thodes dâanalyse dâimage adaptĂ©es. Cette thĂšse de doctorat vise Ă aller au-delĂ de certaines des limites actuelles de lâimagerie CARS dans lâespoir que cela encourage son adoption par un public plus large. Tout dâabord, nous avons introduit un nouveau systĂšme dâimagerie spectrale CARS ayant une vitesse de syntonisation de longueur dâonde beaucoup plus rapide que les autres techniques similaires. Ce systĂšme est basĂ© sur un laser Ă fibre picoseconde synchronisĂ© qui est Ă la fois robuste et portable. Il peut accĂ©der Ă des lignes de vibration Raman sur une plage importante (2700â2950 cm-1) Ă des taux allant jusquâĂ 10 000 points spectrales par seconde. Il est parfaitement adaptĂ© pour lâacquisition dâimages spectrales dans les tissus Ă©pais. En second lieu, nous avons proposĂ© une nouvelle mĂ©thode dâanalyse dâimages pour lâĂ©valuation de la structure de la myĂ©line dans des images de sections longitudinales de moelle Ă©piniĂšre. Nous avons introduit un indicateur quantitatif sensible Ă lâorganisation de la myĂ©line et dĂ©montrĂ© comment il pourrait ĂȘtre utilisĂ© pour Ă©tudier certaines pathologies. Enfin, nous avons dĂ©veloppĂ© une mĂ©thode automatisĂ© pour la segmentation dâaxones myĂ©linisĂ©s dans des images CARS de coupes transversales de tissu nerveux. Cette mĂ©thode a Ă©tĂ© utilisĂ©e pour extraire des informations morphologique des fibres nerveuses dans des images CARS de grande Ă©chelle.Optical-based microscopy techniques can sample biological specimens using many contrast mechanisms providing good sensitivity and high spatial resolution while minimally interfering with the samples. Coherent anti-Stokes Raman scattering (CARS) microscopy is a nonlinear microscopy technique based on the Raman effect. It shares common characteristics of other optical microscopy modalities with the added benefit of providing an endogenous contrast mechanism sensitive to molecular vibrations. CARS is now recognized as a great imaging modality, especially for in vivo experiments since it eliminates the need for exogenous contrast agents, and hence problems related to the delivery, specificity, and invasiveness of those markers. However, there are still several obstacles preventing the wide-scale adoption of CARS in biology and medicine: cost and complexity of current systems as well as difficulty to operate and maintain them, lack of flexibility of the contrast mechanism, low tuning speed and finally, poor accessibility to adapted image analysis methods. This doctoral thesis strives to move beyond some of the current limitations of CARS imaging in the hope that it might encourage a wider adoption of CARS as a microscopy technique. First, we introduced a new CARS spectral imaging system with vibrational tuning speed many orders of magnitude faster than other narrowband techniques. The system presented in this original contribution is based on a synchronized picosecond fibre laser that is both robust and portable. It can access Raman lines over a significant portion of the highwavenumber region (2700â2950 cm-1) at rates of up to 10,000 spectral points per second and is perfectly suitable for the acquisition of CARS spectral images in thick tissue. Secondly, we proposed a new image analysis method for the assessment of myelin health in images of longitudinal sections of spinal cord. We introduced a metric sensitive to the organization/disorganization of the myelin structure and showed how it could be used to study pathologies such as multiple sclerosis. Finally, we have developped a fully automated segmentation method specifically designed for CARS images of transverse cross sections of nerve tissue.We used our method to extract nerve fibre morphology information from large scale CARS images
FlowLens: Seeing Beyond the FoV via Flow-guided Clip-Recurrent Transformer
Limited by hardware cost and system size, camera's Field-of-View (FoV) is not
always satisfactory. However, from a spatio-temporal perspective, information
beyond the camera's physical FoV is off-the-shelf and can actually be obtained
"for free" from the past. In this paper, we propose a novel task termed
Beyond-FoV Estimation, aiming to exploit past visual cues and bidirectional
break through the physical FoV of a camera. We put forward a FlowLens
architecture to expand the FoV by achieving feature propagation explicitly by
optical flow and implicitly by a novel clip-recurrent transformer, which has
two appealing features: 1) FlowLens comprises a newly proposed Clip-Recurrent
Hub with 3D-Decoupled Cross Attention (DDCA) to progressively process global
information accumulated in the temporal dimension. 2) A multi-branch Mix Fusion
Feed Forward Network (MixF3N) is integrated to enhance the spatially-precise
flow of local features. To foster training and evaluation, we establish
KITTI360-EX, a dataset for outer- and inner FoV expansion. Extensive
experiments on both video inpainting and beyond-FoV estimation tasks show that
FlowLens achieves state-of-the-art performance. Code will be made publicly
available at https://github.com/MasterHow/FlowLens.Comment: Code will be made publicly available at
https://github.com/MasterHow/FlowLen
Recommended from our members
New PDE models for imaging problems and applications
Variational methods and Partial Differential Equations (PDEs) have been extensively employed for the mathematical formulation of a myriad of problems describing physical phenomena such as heat propagation, thermodynamic transformations and many more. In imaging, PDEs following variational principles are often considered. In their general form these models combine a regularisation and a data fitting term, balancing one against the other appropriately. Total variation (TV) regularisation is often used due to its edgepreserving and smoothing properties. In this thesis, we focus on the design of TV-based models for several different applications. We start considering PDE models encoding higher-order derivatives to overcome wellknown TV reconstruction drawbacks. Due to their high differential order and nonlinear nature, the computation of the numerical solution of these equations is often challenging. In this thesis, we propose directional splitting techniques and use Newton-type methods that despite these numerical hurdles render reliable and efficient computational schemes. Next, we discuss the problem of choosing the appropriate data fitting term in the case when multiple noise statistics in the data are present due, for instance, to different acquisition and transmission problems. We propose a novel variational model which encodes appropriately and consistently the different noise distributions in this case. Balancing the effect of the regularisation against the data fitting is also crucial. For this sake, we consider a learning approach which estimates the optimal ratio between the two by using training sets of examples via bilevel optimisation. Numerically, we use a combination of SemiSmooth (SSN) and quasi-Newton methods to solve the problem efficiently. Finally, we consider TV-based models in the framework of graphs for image segmentation problems. Here, spectral properties combined with matrix completion techniques are needed to overcome the computational limitations due to the large amount of image data. Further, a semi-supervised technique for the measurement of the segmented region by means of the Hough transform is proposed
Computational imaging and automated identification for aqueous environments
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling 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 fication 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 effects 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 efficacy of the
prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008