{"title":"Reconstruction of a Complex-Valued Field Using the Hilbert-Hankel Transform1","authors":"M. Wengrovitz, A. Oppenheim, G. Frisk","doi":"10.1364/srs.1986.fd1","DOIUrl":"https://doi.org/10.1364/srs.1986.fd1","url":null,"abstract":"A well-known property in Fourier transform theory is that causality in one domain implies real-part sufficiency in the other domain. This property is the basis for the fact that the real and imaginary parts of a signal are related via the Hilbert transform, if the spectrum of the signal is causal. In wave propagation problems involving circular symmetry, it is the circularly symmetric two-dimensional Fourier transform, or equivalently the Hankel transform, which is of central importance. Because of the circular symmetry in such problems, the condition of causality is not applicable. However, in our work we have shown that under some circumstances, it is possible to relate the real and imaginary parts of a propagating field described by a Hankel transform. In this paper, an approximate real-part sufficiency condition for the Hankel transform is developed and an algorithm for reconstructing the real (or imaginary) component from the imaginary (or real) component is applied to synthetic and experimental underwater acoustic fields.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1986-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129151533","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":"Direct Pulse Recording for Reconstruction of Bistatic Synthetic Aperture Radar Images","authors":"A. Gabriel, R. Goldstein","doi":"10.1364/srs.1986.fd4","DOIUrl":"https://doi.org/10.1364/srs.1986.fd4","url":null,"abstract":"An imaging experiment was performed utilizing a synthetic aperture radar tansmitter on the space shuttle and a receiver on an airplane. Because of this \"bistatic\" geometry, focusing is considerably more difficult than in a conventional monastatic SAR. Because the transmitter and receiver are moving relative to each other, the phase history of any target in the scene depends on its location in both the along-track (azimuth) and the cross-track (range) directions, and focusing thus depends on location in the image. As a result, the image must be generated from a collection of separately focused sub-images.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","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":"116790874","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":"Phase Retrieval: Algorithm Improvements, Uniqueness, and Complex Objects","authors":"J. Fienup","doi":"10.1364/srs.1986.tha2","DOIUrl":"https://doi.org/10.1364/srs.1986.tha2","url":null,"abstract":"Since the first topical meeting on Signal Recovery and Synthesis [1], significant new developments have taken place in phase retrieval. Some of these are discussed in other papers in the Digest for this meeting, Signal Recovery and Synthesis II. In this paper we review recent developments in phase retrieval and image reconstruction that revolve around around the use of the iterative Fourier transform algorithm [2-4].","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"99 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":"129317500","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":"A Simple Method for Superresolution","authors":"P. De Santis, F. Gori, G. Guattari, C. Palma","doi":"10.1364/srs.1986.fa3","DOIUrl":"https://doi.org/10.1364/srs.1986.fa3","url":null,"abstract":"Since the invention of superresolving pupils by Toraldo di Francia in 1952 [1] , superresolution has attracted the attention of many authors. As it is well known, the possibility of superresolution rests on some \"a priori\" information about the object. In the case of superresolving pupils, the only prior information is that the object has a finite extent. Elegant as they are from the theoretical point of view, the superresolving pupils [2] seem difficult to fabricate. As a matter of fact, rather severe tolerance conditions should be met in the fabrication and, to our knowledge, no practical superresolving pupil has been produced so far.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"23 8 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":"116375127","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":"A New Technique for Constrained Image Restoration by Compensating the PSF","authors":"E. Malaret, C. McGillem","doi":"10.1364/srs.1986.wa4","DOIUrl":"https://doi.org/10.1364/srs.1986.wa4","url":null,"abstract":"The idea behind the method of constrained image restoration by compensating the point spread function (PSF) is to obtain a restoration filter such that when it is convolved with the PSF the resulting function, called the composite point spread function (CPSF), satisfies appropriate optimization criteria. Ideally it would be desirable to obtain a CPSF that is a delta function; i.e., the restoration filter would be the inverse filter. However this is not possible due to the instability and serious noise amplification of such filters. Stable filters using this approach can be obtained by minimizing an appropriate width measure while constraining the output noise power [1-3]. There are significant advantages in using this method for image (or signal) restoration over other commonly employed procedures. First, the restoration operator can be constrained to a specific size, thereby controlling the duration of transients due to edge effects and reducing the computational burden. Second, the procedure is not dependent on statistics of the image but only on the sensor PSF, the noise, and the sampling grid. Third, for image enhancement it is possible to combine the interpolation and deconvolution procedures into a single operation, thereby increasing the speed and efficiency of the processing.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"118 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":"116375946","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":"Phase Retrieval for Discrete Functions with Support Constraints: Summary","authors":"T. Crimmins","doi":"10.1364/JOSAA.4.000124","DOIUrl":"https://doi.org/10.1364/JOSAA.4.000124","url":null,"abstract":"The phase retrieval problem, i.e., the problem of reconstructing a function from its Fourier modulus or, equivalently, from its autocorrelation function, arises in many fields, e.g., astronomy, wave-front sensing, X-ray crystallography, electron microscopy, particle scattering and pupil-function determination. Here we consider the case in which the object function is assumed to be defined on a two-dimensional discrete grid of sample points.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"7 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":"127667591","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":"Error Lower Bound for Phase Retrieval","authors":"J. Cederquist","doi":"10.1364/srs.1986.thc1","DOIUrl":"https://doi.org/10.1364/srs.1986.thc1","url":null,"abstract":"In phase retrieval problems, it is desired to estimate the phase of the Fourier transform of an object given measurements of the modulus (square root of intensity) of the Fourier transform. This is equivalent to estimating the object itself because of the Fourier transform relationship. Several iterative Fourier transform algorithms have had great success in making such object estimates from Fourier magnitude data and object constraint information [1,2]. However, other than through empirical results [3], it has not been known how the error in the object estimate depends on measurement noise, constraint information, and other parameters describing the problem.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"57 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":"134065962","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":"A Cellular Automata Method for Phase Unwrapping","authors":"D. Ghiglia, G. Mastin, L. Romero","doi":"10.1364/JOSAA.4.000267","DOIUrl":"https://doi.org/10.1364/JOSAA.4.000267","url":null,"abstract":"Cellular automata are simple, discrete mathematical systems that can exhibit complex behavior resulting from collective effects of a large number of cells, each of which evolve in discrete time steps according to rather simple local neighborhood rules.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"19 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131726235","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":"Exploring the Irreducible Support","authors":"B. Brames","doi":"10.1364/srs.1986.thc3","DOIUrl":"https://doi.org/10.1364/srs.1986.thc3","url":null,"abstract":"The phase problem confronts one with the task obtaining a function having a specified autocorrelation (or Fourier modulus) which is also consistent with a limited number of object constraints. In many case of interest it is known that the object has compact support; indeed, in two dimensions this is often sufficient to uniquely define it [1]. Nonetheless, one can usually find an autocorrelation such that some object defined on the same compact support is but one of a multitude of functions having that autocorrelation. However, if the object happens to be defined on Eisenstein's support, it has been shown that the object is always uniquely described by its autocorrelation [2]. We shall call supports of the Eisenstein-type irreducible supports. Unfortunately, variations on Eisenstein's support are the only ones to have been shown to be irreducible, and the method of proof for Eisenstein's support does not lend itself to ready generalization. Herein the irreducible support will be defined in a wider sense, and a method of testing an arbitrary convex support for irreducibility will be presented.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"21 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":"115639645","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":"Two dimensional image restoration using Linear Programming","authors":"R. Mammone, R. Rothacker","doi":"10.1364/srs.1986.wa3","DOIUrl":"https://doi.org/10.1364/srs.1986.wa3","url":null,"abstract":"In this paper we address the issues involved in implementing the Linear Programming (LP) method of image restoration in two dimensions. A modified pivot strategy is introduced in order to reduce the number of iterations. This approach is necessary due to the number of arithmetic operations required per iteration for two dimensional data. We shall also discuss the effects of additive noise, such as that due to quantization. The effects of noise on the performance of restorations with both separable and nonseparable degradations will be presented. The performance of the LP approach has previously been seen to show a preference for sparse images C13, i.e. images with many zero valued pixels. This preference is also found in the two dimensional case. The error in the LP restored image is shown to be less for sparse images than it is for dense images with the same signal-to-noise ratio (SNR). Linear Programming, despite its name, is nonlinear. That is, the LP solution of the sum of two images is not necessarily the sum of the two solutions obtained for each image separately. The LP method also allows for inequality as well as equality constraints to be imposed on the solution. The advantage in performance of the constrained nonlinear approach taken here over linear non-constrained methods is also demonstrated. This is accompli shed by illustrating the pseudo-inverse solution for each LP restoration. The pseudo-inverse method represents the optimal non-constrained linear restoration.","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","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":"129089433","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}