Automatic kernel design procedure for Cohen's bilinear class of representations as applied to in-line Fresnel holograms

2000-01-15
Ozgen, MT
Cohen's bilinear class of shift-invariant space-frequency representations provides an automated means for extracting three-dimensional particle locations from in-line Fresnel holograms without any focusing. Choosing kernel parameters of a fixed-kernel representation in order to achieve the trade-off between auto-term sharpness and cross-term suppression while processing a multiple-particle hologram is a tedious task, especially if the hologram considered is crowded. Hence, this paper proposes an automatic kernel design procedure in order to eliminate this parameter selection task altogether and obtain a signal adaptive representation that matches the particular hologram analyzed. An ambiguity function (AF) domain analysis of a two-dimensional (2-D), multiple-particle hologram reveals AF slices of it that carry the auto-term information. By applying the Radon transform (RT) and the inverse RT to moduli of these slices successively, a 2-D discrete AF domain kernel that matches the hologram is obtained in separable form. This procedure is used in our fixed-frequency slice technique recently proposed for 2-D holograms, and also in computing complete space-frequency patterns for one-dimensional holograms, for particle-location analysis of them.
OPTICS COMMUNICATIONS

Suggestions

Cohen's bilinear class of shift-invariant space/spatial-frequency signal representations for particle-location analysis of in-line Fresnel holograms
Ozgen, MT; Demirbaş, Kerim (The Optical Society, 1998-08-01)
The Cohen bilinear class of shift-invariant space-frequency representations provides an automated means for extracting three-dimensional particle locations from in-line holograms without any focusing. For two-dimensional holograms a fixed-frequency slice technique, based on examining, concurrently, the zero-frequency slice and a nonzero-frequency slice of the two-dimensional representation used, is developed for particle-location analysis. The trade-off between auto-term sharpness and cross-term suppression...
Anlık Spektral Görüntüleme için Tasarım Eniyileme
Ayazgök, Suleyman; Öktem, Sevinç Figen (2019-08-22)
Snapshot spectral imaging enables to reconstructspectral images from a multiplexed single-shot measurement.Since an inversion is required to form the spectral images com-putationally, quantitative characterization of their performanceis essential to optimize the design. In this paper, we analyze theoptimal design of a snapshot spectral imaging technique. Thissnapshot multi-spectral imaging technique uses a diffractive lenscalled generalized photon sieve, and vari...
Extraction of 3D transform and scale invariant patches from range scans
Akagunduz, Erdern; Ulusoy, İlkay (2007-06-22)
An algorithm is proposed to extract transformation and scale invariant 3D fundamental elements from the surface structure of 3D range scan data. The surface is described by mean and Gaussian curvature values at every data point at various scales and a scale-space search is performed in order to extract the fundamental structures and to estimate the location and the scale of each fundamental structure. The extracted fundamental structures can later be used as nodes in a topological graph where the links betw...
Simple method for particle tracing in 2-D unsteady flows
Tuncer, İsmail Hakkı (null; 1997-01-01)
A particle tracing method integrated into an unsteady Navier-Stokes solver with structured 2-D grids is presented. Particles may be released anywhere in the flowfield and are traced in time by convecting them with the local flow velocity. The localization of the particles is based on a sequential and directional search algorithm. The search algorithm provides interpolation weights at the localization point, which is needed to evaluate the local flow velocity. The method is applied to unsteady flow-fields ov...
DEEP LEARNING-BASED UNROLLED RECONSTRUCTION METHODS FOR COMPUTATIONAL IMAGING
Bezek, Can Deniz; Öktem, Sevinç Figen; Department of Electrical and Electronics Engineering (2021-9-08)
Computational imaging is the process of forming images from indirect measurements using computation. In this thesis, we develop deep learning-based unrolled reconstruction methods for various computational imaging modalities. Firstly, we develop two deep learning-based reconstruction methods for diffractive multi-spectral imaging. The first approach is based on plug-and-play regularization with deep denoisers whereas the second one is an end-to-end learned reconstruction based on unrolling. Secondly, we con...
Citation Formats
M. Ozgen, “Automatic kernel design procedure for Cohen’s bilinear class of representations as applied to in-line Fresnel holograms,” OPTICS COMMUNICATIONS, pp. 51–67, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/63886.