Periodic gratings have frequently been used for conversion of modes in highly overmoded circular waveguides [1, 2, 3]. These gratings are formed by periodically varying the waveguide radius resulting in a rippled wall structure and are usually analyzed by coupled mode theory. Very high efficiencies have been reported for these gratings but their lengths remain large compared to the waveguide transverse dimension. Various techniques have been implemented to optimize the length of these gratings [3, 4, 5], but the overall conversion length remains limited by the grating period, δ = 2π/|β m – β n |, where β m and β n are the propagation constants for the input and the output modes. The smallest conversion length reported for a TE02 to TE01 mode converter at 60 GHz is equal to one grating period [4]. This converter was designed for a highly overmoded waveguide with a diameter of 2.771 cm using the coupled mode equations. The efficiency reported for this converter is 97.6%.
周期光栅经常用于高过模圆波导中的模式转换[1,2,3]。这些光栅是通过周期性地改变波导半径而形成波纹壁结构，通常用耦合模式理论进行分析。据报道，这些光栅的效率非常高，但与波导横向尺寸相比，它们的长度仍然很大。已经实现了各种技术来优化这些光栅的长度[3,4,5]，但总的转换长度仍然受到光栅周期的限制，δ = 2π/|β m - β n |，其中β m和β n是输入和输出模式的传播常数。据报道，60 GHz的TE02到TE01模式转换器的最小转换长度等于一个光栅周期[4]。利用耦合模式方程，设计了直径为2.771 cm的高过模波导转换器。该转换器的效率为97.6%。
{"title":"Aperiodic Grating for TE02 to TE01 Conversion in a Highly Overmoded Circular Waveguide","authors":"Tanveer ul Haq, K. Webb, N. Gallagher","doi":"10.1364/srs.1995.rtuc2","DOIUrl":"https://doi.org/10.1364/srs.1995.rtuc2","url":null,"abstract":"Periodic gratings have frequently been used for conversion of modes in highly overmoded circular waveguides [1, 2, 3]. These gratings are formed by periodically varying the waveguide radius resulting in a rippled wall structure and are usually analyzed by coupled mode theory. Very high efficiencies have been reported for these gratings but their lengths remain large compared to the waveguide transverse dimension. Various techniques have been implemented to optimize the length of these gratings [3, 4, 5], but the overall conversion length remains limited by the grating period, δ = 2π/|β\u0000 m\u0000 – β\u0000 n\u0000 |, where β\u0000 m\u0000 and β\u0000 n\u0000 are the propagation constants for the input and the output modes. The smallest conversion length reported for a TE02 to TE01 mode converter at 60 GHz is equal to one grating period [4]. This converter was designed for a highly overmoded waveguide with a diameter of 2.771 cm using the coupled mode equations. The efficiency reported for this converter is 97.6%.","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":"129268597","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}
Often the state or calibration of a measurement system is not known perfectly. The general concept of measurement diversity is the attempt to jointly estimate the state of the system and the object of the measurement by making multiple measurements while perturbing the state of the system in a known fashion. Phase diversity [1] is a well established example of measurement diversity. In phase diversity, the unknown phase aberrations and fine-resolution image are estimated from an in-focus and an intentionally defocused image. In this case the imaging system is perturbed by introducing a quadratic defocus term in the phase aberration.
{"title":"Fine-Resolution Multispectral Imaging Using Wavelength Diversity","authors":"B. Thelen, M. F. Reiley, R. Paxman","doi":"10.1364/srs.1995.rtud3","DOIUrl":"https://doi.org/10.1364/srs.1995.rtud3","url":null,"abstract":"Often the state or calibration of a measurement system is not known perfectly. The general concept of measurement diversity is the attempt to jointly estimate the state of the system and the object of the measurement by making multiple measurements while perturbing the state of the system in a known fashion. Phase diversity [1] is a well established example of measurement diversity. In phase diversity, the unknown phase aberrations and fine-resolution image are estimated from an in-focus and an intentionally defocused image. In this case the imaging system is perturbed by introducing a quadratic defocus term in the phase aberration.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"22 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":"128206981","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 the recovery of some unknown part of a crystal structure from a knowledge of another part of the structure and X-ray diffraction intensities is a familiar problem in crystallography. Amongst the techniques developed for tackling this problem is the difference Fourier method. Another one, recently proposed by Szöke [1], is based on an analogy with holography [2].
{"title":"Holographic Reconstruction for Macromolecular Structure Completion in X-Ray Crystallography by Iterative Applications of Linear Programming","authors":"D. Wild, X. Chen, D. Saldin","doi":"10.1364/srs.1995.rtue6","DOIUrl":"https://doi.org/10.1364/srs.1995.rtue6","url":null,"abstract":"The problem of the recovery of some unknown part of a crystal structure from a knowledge of another part of the structure and X-ray diffraction intensities is a familiar problem in crystallography. Amongst the techniques developed for tackling this problem is the difference Fourier method. Another one, recently proposed by Szöke [1], is based on an analogy with holography [2].","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"70 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":"125216044","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 short-exposure solar image observed on the ground is given as the convolution of a solar surface structure and an instantaneous point-spread-function (PSF) of an atmosphere-telescope system 1. Thus, one can hardly observe finer structure on the solar surface than a seeing-disk size. The iterative blind deconvolution (BD) proposed by Ayers and Dainty 2 is a powerful tool for recovering the solar image atmospherically degraded. We have proposed two BD methods based on the iterative BD methods, multiframe BD 3 and segment-image BD 4. The former method consists of the application of the iterative BD method to several frames observed at different times. In the latter method, the iterative BD method is applied to images segmented from a single frame. In this paper, we present a BD method using images segmented from multiple frames, referred to as a segmented-multiframe BD (SMBD) method, which is a fusion of the previous two methods.
{"title":"Solar Imaging by Blind Deconvolution of Segments from Multiple frames","authors":"N. Miura, N. Baba, F. Tsumuraya, T. Sakurai","doi":"10.1364/srs.1995.rtue1","DOIUrl":"https://doi.org/10.1364/srs.1995.rtue1","url":null,"abstract":"A short-exposure solar image observed on the ground is given as the convolution of a solar surface structure and an instantaneous point-spread-function (PSF) of an atmosphere-telescope system 1. Thus, one can hardly observe finer structure on the solar surface than a seeing-disk size. The iterative blind deconvolution (BD) proposed by Ayers and Dainty 2 is a powerful tool for recovering the solar image atmospherically degraded. We have proposed two BD methods based on the iterative BD methods, multiframe BD 3 and segment-image BD 4. The former method consists of the application of the iterative BD method to several frames observed at different times. In the latter method, the iterative BD method is applied to images segmented from a single frame. In this paper, we present a BD method using images segmented from multiple frames, referred to as a segmented-multiframe BD (SMBD) method, which is a fusion of the previous two methods.","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":"131315815","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}
Many imaging systems in use today acquire data that are related to a desired object function f(·) through the linear relationship where h(·,·; θ) is the point-spread function for the imaging system, and θ is a collection of system parameters – some or all of which may be unknown – that characterize the system and, hence, its point-spread function. In some situations, only a small number of parameters might be required for the characterization of the system, whereas in other situations, the parameters that characterize the system might be made up of the point-spread function’s point-by-point values for all x and y of interest.
{"title":"Blind Deblurring and Deconvolution","authors":"T. Schulz","doi":"10.1364/srs.1995.rwa1","DOIUrl":"https://doi.org/10.1364/srs.1995.rwa1","url":null,"abstract":"Many imaging systems in use today acquire data that are related to a desired object function f(·) through the linear relationship where h(·,·; θ) is the point-spread function for the imaging system, and θ is a collection of system parameters – some or all of which may be unknown – that characterize the system and, hence, its point-spread function. In some situations, only a small number of parameters might be required for the characterization of the system, whereas in other situations, the parameters that characterize the system might be made up of the point-spread function’s point-by-point values for all x and y of interest.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"44 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":"114524528","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}
Frequency-resolved optical gating (FROG) is a technique used to measure the intensity and phase of ultrashort laser pulses. The pulse to be measured is split into probe and gate pulses. The gate and probe pulses are combined in a non-linear optical medium to temporally select portions of the probe. The resulting signal is spectrally resolved at various time delays to produce a spectrogram of the probe.1-3 All the time and frequency information about the pulse (probe) is contained in its spectrogram (“FROG trace”). An iterative 2-dimensional phase retrieval algorithm is used to determine the phase of the FROG trace, and hence, the intensity and phase of the pulse.1,2
{"title":"Inversion of Frequency-Resolved Optical Gating (FROG) Spectrograms in Real-Time: A Femtosecond Oscilloscope","authors":"D. Kane","doi":"10.1364/srs.1998.sthd.4","DOIUrl":"https://doi.org/10.1364/srs.1998.sthd.4","url":null,"abstract":"Frequency-resolved optical gating (FROG) is a technique used to measure the intensity and phase of ultrashort laser pulses. The pulse to be measured is split into probe and gate pulses. The gate and probe pulses are combined in a non-linear optical medium to temporally select portions of the probe. The resulting signal is spectrally resolved at various time delays to produce a spectrogram of the probe.1-3 All the time and frequency information about the pulse (probe) is contained in its spectrogram (“FROG trace”). An iterative 2-dimensional phase retrieval algorithm is used to determine the phase of the FROG trace, and hence, the intensity and phase of the pulse.1,2","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"55 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":"129116048","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 imaging of a moving target by a typical CCD or CID camera with a finite integration time and finite-sized detectors presents a challenging problem in modeling and analysis. This problem is further complicated if the target is illuminated with a pulsed laser with a nonuniform temporal profile. However, a robust model of the imaging process can prove invaluable in the definition of system requirements, performance predictions, and in the development of post-detection correction algorithms.
{"title":"Imaging in the presence of temporal distortions","authors":"B. Landesman, David F. Olson","doi":"10.1364/srs.1995.rtud4","DOIUrl":"https://doi.org/10.1364/srs.1995.rtud4","url":null,"abstract":"The imaging of a moving target by a typical CCD or CID camera with a finite integration time and finite-sized detectors presents a challenging problem in modeling and analysis. This problem is further complicated if the target is illuminated with a pulsed laser with a nonuniform temporal profile. However, a robust model of the imaging process can prove invaluable in the definition of system requirements, performance predictions, and in the development of post-detection correction algorithms.","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":"129163318","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}
Frequency-resolved optical gating[1, 2] (FROG) is a technique that uses a phase-retrieval algorithm to obtain the intensity, I(t), and phase, ϕ(t), from a measured spectrogram of the pulse. Previous simulations have shown that, for noise-free data, the algorithm retrieves the correct intensity and phase for all pulses attempted, including those with complex intensity and phase structure. In practice, however, noise is present in actual FROG traces, and here we discuss the effects of noise on FROG and image-processing techniques to improve the retrieval.
{"title":"Noise in Frequency-Resolved-Optical-Gating Measurements of Ultrashort Laser Pulses","authors":"D. Fittinghoff, K. Delong, R. Trebino, C. Ladera","doi":"10.1364/srs.1995.rtud1","DOIUrl":"https://doi.org/10.1364/srs.1995.rtud1","url":null,"abstract":"Frequency-resolved optical gating[1, 2] (FROG) is a technique that uses a phase-retrieval algorithm to obtain the intensity, I(t), and phase, ϕ(t), from a measured spectrogram of the pulse. Previous simulations have shown that, for noise-free data, the algorithm retrieves the correct intensity and phase for all pulses attempted, including those with complex intensity and phase structure. In practice, however, noise is present in actual FROG traces, and here we discuss the effects of noise on FROG and image-processing techniques to improve the retrieval.","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"90 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":"121392211","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 explore the feasibility of a new technique for visualization of the effects of turbulence in clear air [1]-[2], [4]-[5], based on some earlier ideas [10]. Sequences of short exposure images of a scene, such as the surface of the moon or a horizontally imaged scene on the earth, are captured using a 0.4 m diameter optical telescope. The field of view, typically 100 arc secs across, is wide compared to that of most astronomical observations [6]-[8], so that the main effect observed is a random “wobbling” within each image. With an exposure time of between 5 and 10 ms, the atmospheric wobble is “frozen” to provide a sequence of randomly warped images. The point spread function (PSF) for each image, due to the atmosphere and telescope, approximates a position-dependent randomly-displaced delta function (if we temporarily ignore instantaneous speckle and instrument blurring).
{"title":"Visualization of turbulence and motion-blur removal in wide-area imaging through the atmosphere","authors":"D. Fraser, G. Thorpe, Andrew Lambert","doi":"10.1364/srs.1998.stub.1","DOIUrl":"https://doi.org/10.1364/srs.1998.stub.1","url":null,"abstract":"We explore the feasibility of a new technique for visualization of the effects of turbulence in clear air [1]-[2], [4]-[5], based on some earlier ideas [10]. Sequences of short exposure images of a scene, such as the surface of the moon or a horizontally imaged scene on the earth, are captured using a 0.4 m diameter optical telescope. The field of view, typically 100 arc secs across, is wide compared to that of most astronomical observations [6]-[8], so that the main effect observed is a random “wobbling” within each image. With an exposure time of between 5 and 10 ms, the atmospheric wobble is “frozen” to provide a sequence of randomly warped images. The point spread function (PSF) for each image, due to the atmosphere and telescope, approximates a position-dependent randomly-displaced delta function (if we temporarily ignore instantaneous speckle and instrument blurring).","PeriodicalId":184407,"journal":{"name":"Signal Recovery and Synthesis","volume":"17 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":"126949862","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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