In brightfield, phase-contrast or polarization microscopy, the image can be modeled by using scattering theory. The object, consisting of spatial variations in complex refractive index, scatters components of an angular spectrum of plane waves, and the image calculated by integration over incident and scattered waves. This approach takes into account the high aperture effects, important in microscope imaging. Rigorous methods can be used to calculate the scattering by the object.1 However, these methods, in addition to being in general very computationally intensive, result in the disadvantges that it is difficult to see trends in the behaviour and usually impracticable to reconstruct the object from the image data.
{"title":"Microscope image reconstruction","authors":"C. Sheppard","doi":"10.1364/srs.1998.stue.2","DOIUrl":"https://doi.org/10.1364/srs.1998.stue.2","url":null,"abstract":"In brightfield, phase-contrast or polarization microscopy, the image can be modeled by using scattering theory. The object, consisting of spatial variations in complex refractive index, scatters components of an angular spectrum of plane waves, and the image calculated by integration over incident and scattered waves. This approach takes into account the high aperture effects, important in microscope imaging. Rigorous methods can be used to calculate the scattering by the object.1 However, these methods, in addition to being in general very computationally intensive, result in the disadvantges that it is difficult to see trends in the behaviour and usually impracticable to reconstruct the object from the image data.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"26 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":"121834541","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 problem of blind deconvolution, extracting both the original image and point spread function (PSF) from only the measurement of their convolution, may at first seem a futile task. However Lane and Bates [1] have shown via separation of zero sheets that blind deconvolution is theoretically possible for dimensions of two or greater when very little noise is present. In practice noise is a significant problem that limits the ability to perform successful deconvolution. Many researchers have also found that the only way of achieving blind deconvolution is to impose spatial and spectral constraints on the solution, derived from a priori information about the imaging system, and to eliminate the trivial solution of the image or PSF being a delta function. This becomes less useful in situations where very little else is known except the measured data.
{"title":"Multiframe iterative blind deconvolution using only a positivity constraint","authors":"D. Biggs, M. Andrews","doi":"10.1364/srs.1998.stua.3","DOIUrl":"https://doi.org/10.1364/srs.1998.stua.3","url":null,"abstract":"The problem of blind deconvolution, extracting both the original image and point spread function (PSF) from only the measurement of their convolution, may at first seem a futile task. However Lane and Bates [1] have shown via separation of zero sheets that blind deconvolution is theoretically possible for dimensions of two or greater when very little noise is present. In practice noise is a significant problem that limits the ability to perform successful deconvolution. Many researchers have also found that the only way of achieving blind deconvolution is to impose spatial and spectral constraints on the solution, derived from a priori information about the imaging system, and to eliminate the trivial solution of the image or PSF being a delta function. This becomes less useful in situations where very little else is known except the measured data.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"258 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":"122469136","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}
It is well known that the image forming ability of an astronomical optic system is limited by distortions due to a turbulent atmosphere1. A large aperture telescope will have no more resolving power than one with a diameter equal to Fried’s rO parameter which is usually between 5 and 10 centimeters. Recently, technological development in the areas of wave-front sensing and deformable mirrors have lead to adaptive optic systems. These systems attempt to measure the atmosphere induced wave-front distortion and cancel it through the use of a deformable mirror. Due to the short correlation time of the atmosphere, they require a high bandwidth multi-input multi-output control system.
{"title":"High Resolution Imaging of Astronomical Objects Using Deconvolution from Wave-Front Sensing Experimental Demonstration","authors":"D. Dayton, S. Sandven, J. Gonglewski","doi":"10.1364/srs.1995.rwa4","DOIUrl":"https://doi.org/10.1364/srs.1995.rwa4","url":null,"abstract":"It is well known that the image forming ability of an astronomical optic system is limited by distortions due to a turbulent atmosphere1. A large aperture telescope will have no more resolving power than one with a diameter equal to Fried’s rO parameter which is usually between 5 and 10 centimeters. Recently, technological development in the areas of wave-front sensing and deformable mirrors have lead to adaptive optic systems. These systems attempt to measure the atmosphere induced wave-front distortion and cancel it through the use of a deformable mirror. Due to the short correlation time of the atmosphere, they require a high bandwidth multi-input multi-output control system.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"38 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":"122938947","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}
Atmospheric aerosols, although only a minor constituent of the earth’s atmosphere, play an important role in many atmospheric processes. Due to their appreciable influence on the earth’s radiation budget, air quality, clouds and precipitation as well as the chemistry of the troposphere and stratosphere it is necessary to gather detailed information on their optical and physical properties. A multiple-wavelength lidar as well as a Raman lidar at the Institute provide optical particle information in terms of six backscatter coefficients and two extinction coefficients in the wavelength range from 0.355 to 1.064 μm on a vertical scale. A data-evaluation algorithm that uses the method of inversion via regularization has been specifically designed to retrieve physical properties from the given optical information. The physical parameters can be described by, e.g., the particle size distributions, the mean sizes derived from it, like the effective radius, the volume, surface-area, and number concentrations as well as the complex refractive index. Due to the low amount of available a priori information on the particle properties in combination with the small number of optical information that additionally include large measurement errors the main focus had been on the retrieval of the mean values. To control the quality of the regularization under these difficult conditions the method of generalized cross-validation is used as it does not require the knowledge of the underlying measurement errors nor the knowledge of the specific shape of the particle size distributions.
{"title":"Retrieval of Physical Properties of Atmospheric Particles by Inversion via Regularization in the Limit of a Small Optical Data Set","authors":"D. Müller, A. Ansmann, U. Wandinger, D. Althausen","doi":"10.1364/srs.1998.sthc.4","DOIUrl":"https://doi.org/10.1364/srs.1998.sthc.4","url":null,"abstract":"Atmospheric aerosols, although only a minor constituent of the earth’s atmosphere, play an important role in many atmospheric processes. Due to their appreciable influence on the earth’s radiation budget, air quality, clouds and precipitation as well as the chemistry of the troposphere and stratosphere it is necessary to gather detailed information on their optical and physical properties. A multiple-wavelength lidar as well as a Raman lidar at the Institute provide optical particle information in terms of six backscatter coefficients and two extinction coefficients in the wavelength range from 0.355 to 1.064 μm on a vertical scale. A data-evaluation algorithm that uses the method of inversion via regularization has been specifically designed to retrieve physical properties from the given optical information. The physical parameters can be described by, e.g., the particle size distributions, the mean sizes derived from it, like the effective radius, the volume, surface-area, and number concentrations as well as the complex refractive index. Due to the low amount of available a priori information on the particle properties in combination with the small number of optical information that additionally include large measurement errors the main focus had been on the retrieval of the mean values. To control the quality of the regularization under these difficult conditions the method of generalized cross-validation is used as it does not require the knowledge of the underlying measurement errors nor the knowledge of the specific shape of the particle size distributions.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"47 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":"133564198","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}
P. Negrete-Regagnon, J. Drummond, R. Fugate, J. Gonglewski, N. Wooder, J. Dainty
The release of military technology in the adaptive optics (AO) and laser beacons fields [1] motivated the astronomical community to become actively involved in its implementation in astronomical sites all around the world. Unfortunately, even with the impressive gain in resolution these systems provide, high-resolution imaging from ground-based telescopes is still far from ideal. Traditional speckle interferometry techniques and post-processing data reduction are probably the only viable way to obtain approximated diffraction-limited images. This work describes the application of a posteriori processing to both AO compensated and uncompensated data and shows the improvement that can be achieved.
{"title":"Bispectral Imaging on Adaptive Optics Compensated Data","authors":"P. Negrete-Regagnon, J. Drummond, R. Fugate, J. Gonglewski, N. Wooder, J. Dainty","doi":"10.1364/srs.1995.rwa3","DOIUrl":"https://doi.org/10.1364/srs.1995.rwa3","url":null,"abstract":"The release of military technology in the adaptive optics (AO) and laser beacons fields [1] motivated the astronomical community to become actively involved in its implementation in astronomical sites all around the world. Unfortunately, even with the impressive gain in resolution these systems provide, high-resolution imaging from ground-based telescopes is still far from ideal. Traditional speckle interferometry techniques and post-processing data reduction are probably the only viable way to obtain approximated diffraction-limited images. This work describes the application of a posteriori processing to both AO compensated and uncompensated data and shows the improvement that can be achieved.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"82 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":"131844766","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 international oil industry spends about $4 billion/year acquiring reflection seismic data. A large survey collects a terabyte (1012) of data which is computer processed to a pixel volume of (103)3 = 109 bytes. At sea the energy source is usually an air gun while on land, the source is mostly buried dynamite, and sometimes, multiple trucks carrying sweep- frequency ground vibrators. At sea, a shot is fired every 10 seconds; echos are recorded along 6 km cables at about a thousand locations, each channel recording a signal of about 2000 floating point values. A typical marine survey contract whose result is shown in Figure 1, lasts a month or more and costs upward of $10M.
{"title":"Inverse problems in reflection seismology","authors":"J. Claerbout","doi":"10.1364/srs.1998.stha.1","DOIUrl":"https://doi.org/10.1364/srs.1998.stha.1","url":null,"abstract":"The international oil industry spends about $4 billion/year acquiring\u0000 reflection seismic data. A large survey collects a terabyte\u0000 (1012) of data which is computer processed to a pixel\u0000 volume of (103)3 = 109 bytes. At sea\u0000 the energy source is usually an air gun while on land, the source is\u0000 mostly buried dynamite, and sometimes, multiple trucks carrying sweep-\u0000 frequency ground vibrators. At sea, a shot is fired every 10 seconds;\u0000 echos are recorded along 6 km cables at about a thousand locations,\u0000 each channel recording a signal of about 2000 floating point values. A\u0000 typical marine survey contract whose result is shown in Figure 1,\u0000 lasts a month or more and costs upward of $10M.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"48 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":"115793395","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}
Motion during the collection of a magnetic resonance imaging (MRI) data set causes phase errors which result in a smearing or ghosting of the image. In this paper we present a new algorithm for correcting translational motion errors. It follows the same philosophy as the gradient search approaches that we invented to determine the aberrations of the Hubble Space Telescope [1] and to correct phase errors for synthetic-aperture radar [2].
{"title":"Support-Constrained Motion-Artifact Correction for Magnetic Resonance Imaging","authors":"J. Fienup, J. E. Van Buhler","doi":"10.1364/srs.1995.rtua4","DOIUrl":"https://doi.org/10.1364/srs.1995.rtua4","url":null,"abstract":"Motion during the collection of a magnetic resonance imaging (MRI) data set causes phase errors which result in a smearing or ghosting of the image. In this paper we present a new algorithm for correcting translational motion errors. It follows the same philosophy as the gradient search approaches that we invented to determine the aberrations of the Hubble Space Telescope [1] and to correct phase errors for synthetic-aperture radar [2].","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":"129621986","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 major characteristic of an optical wave is its coherence function function which is defined by: where V is the complex amplitude and the brackets stand for ensemble averaging.
光波的一个主要特征是它的相干函数，其定义为:其中V是复振幅，括号表示系综平均。
{"title":"Coherence function as carrier of optical information","authors":"A. Lohmann, D. Mendlovic, G. Shabtay","doi":"10.1364/srs.1998.swb.4","DOIUrl":"https://doi.org/10.1364/srs.1998.swb.4","url":null,"abstract":"A major characteristic of an optical wave is its coherence function function which is defined by: where V is the complex amplitude and the brackets stand for ensemble averaging.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"30 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":"128074748","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 ab initio phase problem is the rate-limiting step in X-ray crystallography and by extension is also a rate-limiting step in the determination of high-resolution 3-dimensional (3-D) structures of biological macromolecules. Thus it has dramatic repercussions for the emerging areas of rational drug-design and protein engineering. Simply stated, the problem is of recovering the real-space image of a biological macromolecule - represented in Cartesian 3-D coordinates-from partial Fourier information alone. Specifically, the available information in reciprocal space is that of the Fourier amplitudes; the complementary Fourier phase information is not available.
{"title":"Recovering Fourier phase data from amplitude data alone in macromolecular X-ray crystallography","authors":"S. Subbiah","doi":"10.1364/srs.1995.rtub1","DOIUrl":"https://doi.org/10.1364/srs.1995.rtub1","url":null,"abstract":"The ab initio phase problem is the rate-limiting step in X-ray crystallography and by extension is also a rate-limiting step in the determination of high-resolution 3-dimensional (3-D) structures of biological macromolecules. Thus it has dramatic repercussions for the emerging areas of rational drug-design and protein engineering. Simply stated, the problem is of recovering the real-space image of a biological macromolecule - represented in Cartesian 3-D coordinates-from partial Fourier information alone. Specifically, the available information in reciprocal space is that of the Fourier amplitudes; the complementary Fourier phase information is not available.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"9 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":"127731723","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 diffraction tomography is a new method in which the complex permittivity profile of an object is reconstructed using algorithms that require the knowledge of both the amplitude and the phase of the scattered field [1-2]. However the difficulty of obtaining the phase of the scattered field at optical frequencies has limited the practical applications of diffraction tomography.
{"title":"Material characterization via phaseless tomography : numerical results in phase retrieval","authors":"R. Pierri, G. Leone, R. Bernini","doi":"10.1364/srs.1998.sthc.3","DOIUrl":"https://doi.org/10.1364/srs.1998.sthc.3","url":null,"abstract":"The diffraction tomography is a new method in which the complex\u0000 permittivity profile of an object is reconstructed using algorithms\u0000 that require the knowledge of both the amplitude and the phase of the\u0000 scattered field [1-2]. However the difficulty of\u0000 obtaining the phase of the scattered field at optical frequencies has\u0000 limited the practical applications of diffraction tomography.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"64 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":"127428171","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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