Many incoherent optical/digital systems can be used for non-imaging purposes, such as passive ranging. These systems cannot effectively be analyzed or designed in terms of traditional image-forming systems. Instead, such systems should be analyzed in terms of information theory. Through mathematical modelling of the sampled image, information theory can be used to optimize a given system.
{"title":"An Information Theory Approach To Three Incoherent Information Processing Systems","authors":"E. Dowski","doi":"10.21236/ada299683","DOIUrl":"https://doi.org/10.21236/ada299683","url":null,"abstract":"Many incoherent optical/digital systems can be used for non-imaging purposes, such as passive ranging. These systems cannot effectively be analyzed or designed in terms of traditional image-forming systems. Instead, such systems should be analyzed in terms of information theory. Through mathematical modelling of the sampled image, information theory can be used to optimize a given system.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122378109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We are investigating a novel 3-D imaging modality called Phase Retrieval with an Opacity Constraint for LAser IMaging (PROCLAIM) [1]. PROCLAIM data are collected by illuminating a 3-D object with a laser and making angle-angle intensity measurements of the reflected speckle pattern in the far field. A separate angle-angle intensity measurement is made for each of several laser frequencies. Properly formatted, these data represent samples of the 3-D Fourier intensity (square of the Fourier magnitude) of the illuminated object [2]. A 3-D FFT would give the 3-D autocorrelation of the object. In order to recover a 3-D image we must apply a phase-retrieval algorithm.
{"title":"Phase Retrieval with an Opacity Constraint","authors":"R. Paxman, J. Fienup, J. H. Seldin, J. Marron","doi":"10.1364/srs.1995.rwd2","DOIUrl":"https://doi.org/10.1364/srs.1995.rwd2","url":null,"abstract":"We are investigating a novel 3-D imaging modality called Phase Retrieval with an Opacity Constraint for LAser IMaging (PROCLAIM) [1]. PROCLAIM data are collected by illuminating a 3-D object with a laser and making angle-angle intensity measurements of the reflected speckle pattern in the far field. A separate angle-angle intensity measurement is made for each of several laser frequencies. Properly formatted, these data represent samples of the 3-D Fourier intensity (square of the Fourier magnitude) of the illuminated object [2]. A 3-D FFT would give the 3-D autocorrelation of the object. In order to recover a 3-D image we must apply a phase-retrieval algorithm.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125648107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A physically constrained iterative deconvolution algorithm is applied to both simulated and real artificial satellite (space object) observations obtained with adaptive optical systems. The problems associated with obtaining good point spread function information is discussed and the algorithm applied also permits reconstruction of not only the object but also the corresponding point spread functions.
{"title":"Space Object Identification using a Physically Constrained Iterative Deconvolution Algorithm","authors":"J. Christou, E. Hege, S. Jefferies, M. Cheselka","doi":"10.1364/srs.1998.stub.2","DOIUrl":"https://doi.org/10.1364/srs.1998.stub.2","url":null,"abstract":"A physically constrained iterative deconvolution algorithm is applied to both simulated and real artificial satellite (space object) observations obtained with adaptive optical systems. The problems associated with obtaining good point spread function information is discussed and the algorithm applied also permits reconstruction of not only the object but also the corresponding point spread functions.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127552266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A near-field scanning optical microscope (NSOM) changes the light field on an extremely small area of the sample surface by inserting a probe tip into the near field of the sample surface. Since the probe and the sample are very near each other, much nearer than the wavelength of incident light, near-field imaging is based on multiple scattering or interaction of photons with the total system including probe and sample. The image of an NSOM is, hence, very dependent on the gap distance between probe and sample surface, polarization of light, and nanometric distribution of structure and complex dielectric constant of sample. Nevertheless, theory of image formation has not been established. Currently people are trying to learn, from the numerically simulated experiences, how the image changes by parameters. Girard and Courjon derived representing an NSOM system with a self-consistent approach.1) Novotny et al. showed the imaging characteristics of a two-dimensional NSOM system with different samples and polarizations.2)
{"title":"Image formation and signal recovery in near field optical microscopy","authors":"S. Kawata","doi":"10.1364/srs.1998.stue.1","DOIUrl":"https://doi.org/10.1364/srs.1998.stue.1","url":null,"abstract":"A near-field scanning optical microscope (NSOM) changes the light field on an extremely small area of the sample surface by inserting a probe tip into the near field of the sample surface. Since the probe and the sample are very near each other, much nearer than the wavelength of incident light, near-field imaging is based on multiple scattering or interaction of photons with the total system including probe and sample. The image of an NSOM is, hence, very dependent on the gap distance between probe and sample surface, polarization of light, and nanometric distribution of structure and complex dielectric constant of sample. Nevertheless, theory of image formation has not been established. Currently people are trying to learn, from the numerically simulated experiences, how the image changes by parameters. Girard and Courjon derived representing an NSOM system with a self-consistent approach.1) Novotny et al. showed the imaging characteristics of a two-dimensional NSOM system with different samples and polarizations.2)","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123302728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the last decade or so several papers have introduced and developed the area of tomographic imaging of vector fields. Johnson et al. [1] began the investigation by studying the imaging of flow fields using acoustic time-of-flight measurements. In this measurement, the time of flight is influenced by the component of the field in the direction of propagation, and is not influenced by the orthogonal component. This type of measurement is called a longitudinal measurement. Norton [2] concluded that longitudinal measurements allow the reconstruction of the solenoidal (divergence-free) field component, but not the irrotational (curl-free) component. He suggested using boundary measurements to reconstruct the irrotational component. Braun and Hauck [3] then discovered that a new type of tomographic measurement called the transverse measurement (sensitive to the orthogonal component of flow) allows one to reconstruct the irrotational component without boundary measurements. Prince synthesized these discoveries and extended the results to three dimensions in [4]. New algorithms for reconstruction using convolution backprojection have also been proposed in [5].
{"title":"Tomographic Imaging of Vector Fields","authors":"Jerry L Prince","doi":"10.1364/srs.1995.rtua1","DOIUrl":"https://doi.org/10.1364/srs.1995.rtua1","url":null,"abstract":"In the last decade or so several papers have introduced and developed the area of tomographic imaging of vector fields. Johnson et al. [1] began the investigation by studying the imaging of flow fields using acoustic time-of-flight measurements. In this measurement, the time of flight is influenced by the component of the field in the direction of propagation, and is not influenced by the orthogonal component. This type of measurement is called a longitudinal measurement. Norton [2] concluded that longitudinal measurements allow the reconstruction of the solenoidal (divergence-free) field component, but not the irrotational (curl-free) component. He suggested using boundary measurements to reconstruct the irrotational component. Braun and Hauck [3] then discovered that a new type of tomographic measurement called the transverse measurement (sensitive to the orthogonal component of flow) allows one to reconstruct the irrotational component without boundary measurements. Prince synthesized these discoveries and extended the results to three dimensions in [4]. New algorithms for reconstruction using convolution backprojection have also been proposed in [5].","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"15 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132531960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Image Reconstruction and Information","authors":"R. Blahut","doi":"10.1364/srs.1995.rwc1","DOIUrl":"https://doi.org/10.1364/srs.1995.rwc1","url":null,"abstract":"Summary not available.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131735766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Astronomers have long known that the resolution in ground-based astronomy is usually limited by aberrations introduced by the atmosphere. Over the years, researchers have developed a variety of clever pre- and post-detection approaches for correcting these effects, each with its own merits and regimes of operation. These approaches include stellar speckle imaging, deconvolution from wavefront sensing, and adaptive optics. We have been investigating a novel data-collection and processing approach for combating the effects of atmospheric seeing called phase-diverse speckle imaging.
{"title":"Simulation Validation of Phase-Diverse Speckle Imaging","authors":"R. Paxman, J. H. Seldin","doi":"10.1364/srs.1995.rwb2","DOIUrl":"https://doi.org/10.1364/srs.1995.rwb2","url":null,"abstract":"Astronomers have long known that the resolution in ground-based astronomy is usually limited by aberrations introduced by the atmosphere. Over the years, researchers have developed a variety of clever pre- and post-detection approaches for correcting these effects, each with its own merits and regimes of operation. These approaches include stellar speckle imaging, deconvolution from wavefront sensing, and adaptive optics. We have been investigating a novel data-collection and processing approach for combating the effects of atmospheric seeing called phase-diverse speckle imaging.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124287078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Fourier transform of a signal or image of compact support is an entire function of exponential type which can be represented in terms of their zeros by means of a product of factors each encoding a zero of the function. In more than one dimensional problems, an entire function is generally not factorizable into an infinite product of terms, but is irreducible, [1,2] from which it follows that there is a unique phase to be associated with its power spectrum, in principle.
{"title":"Blind deconvolution and phase retrieval using point zeros","authors":"Pi-tung Chen, M. Fiddy, A. Greenaway, D. Pommet","doi":"10.1364/srs.1995.rwd3","DOIUrl":"https://doi.org/10.1364/srs.1995.rwd3","url":null,"abstract":"The Fourier transform of a signal or image of compact support is an entire function of exponential type which can be represented in terms of their zeros by means of a product of factors each encoding a zero of the function. In more than one dimensional problems, an entire function is generally not factorizable into an infinite product of terms, but is irreducible, [1,2] from which it follows that there is a unique phase to be associated with its power spectrum, in principle.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121765810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A coherently illuminated diffuse scatterer (object) gives rise to ensemble field statistics at spatial locations in the Fraunhofer plane of the object modeled by circularly complex Gaussian random variables [ref 1]. The joint probability density function for the phase is used to determine the ensemble phase correlation function at two arbitrary spatial locations in the object’s Fraunhofer plane (the entrance pupil plane of a telescope imaging the object). The phase correlation function is used in conjunction with a minimum variance technique to obtain an optimum solution matrix mapping phase difference measurements to phase estimates at arbitrary points in the pupil. Expressions for the expected mean squared phase error are developed for the minimum variance technique and compared with conventional Least Mean Squared reconstructors. Phase estimates using the minimum variance technique and known amplitudes are used to reconstruct the image for simple targets.
{"title":"Coherent Wavefront Reconstruction Using Object Statistics","authors":"W. Arrasmith, M. Roggemann, B. Welsh","doi":"10.1364/srs.1995.rwc4","DOIUrl":"https://doi.org/10.1364/srs.1995.rwc4","url":null,"abstract":"A coherently illuminated diffuse scatterer (object) gives rise to ensemble field statistics at spatial locations in the Fraunhofer plane of the object modeled by circularly complex Gaussian random variables [ref 1]. The joint probability density function for the phase is used to determine the ensemble phase correlation function at two arbitrary spatial locations in the object’s Fraunhofer plane (the entrance pupil plane of a telescope imaging the object). The phase correlation function is used in conjunction with a minimum variance technique to obtain an optimum solution matrix mapping phase difference measurements to phase estimates at arbitrary points in the pupil. Expressions for the expected mean squared phase error are developed for the minimum variance technique and compared with conventional Least Mean Squared reconstructors. Phase estimates using the minimum variance technique and known amplitudes are used to reconstruct the image for simple targets.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129471266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In most phase reconstruction algorithms1-6, two generalized intensity functions Ic(x,y) and Is(x,y), shown in Fig. 1, are obtained after some initial processing steps, independently of the specific experimental technique used to obtain the modulated intensity patterns. Generalized intensity with the background intensity subtracted is coded in the levels of gray.
{"title":"Convergent, recursive phase reconstruction with synthetic interferograms","authors":"G. Páez, M. Strojnik","doi":"10.1364/srs.1998.sthd.5","DOIUrl":"https://doi.org/10.1364/srs.1998.sthd.5","url":null,"abstract":"In most phase reconstruction algorithms1-6, two generalized intensity functions Ic(x,y) and Is(x,y), shown in Fig. 1, are obtained after some initial processing steps, independently of the specific experimental technique used to obtain the modulated intensity patterns. Generalized intensity with the background intensity subtracted is coded in the levels of gray.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122764941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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