Normalization and noise-reduction algorithm for fringe patterns

Ochoa, Noé Alcalá; Silva-Moreno, Alejandra A.

doi:10.1016/j.optcom.2006.09.062

Cited by 36 publications

(18 citation statements)

References 15 publications

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“…Several normalization solutions have been proposed over the last decade. They include, to mention some of them, interferogram clipping [4,5], 2-D envelope transform coupled with spin filtering [8], two orthogonal bandpass filtering [9], the quadrature transform isotropic operator (Hilbert transform) algorithm [10], adaptive filtering as a linear combination of isotropic bandpass filtering [11], continuous wavelet transform (CWT) processing coupled with synthesized interferogram clipping [12,13], the directional derivative approach [14], and the bidimensional empirical mode decomposition aided by the partial Hilbert transformation [15].…”

Section: Introductionmentioning

confidence: 99%

Adaptive enhancement of optical fringe patterns by selective reconstruction using FABEMD algorithm and Hilbert spiral transform

2012

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Presented method for fringe pattern enhancement has been designed for processing and analyzing low quality fringe patterns. It uses a modified fast and adaptive bidimensional empirical mode decomposition (FABEMD) for the extraction of bidimensional intrinsic mode functions (BIMFs) from an interferogram. Fringe pattern is then selectively reconstructed (SR) taking the regions of selected BIMFs with high modulation values only. Amplitude demodulation and normalization of the reconstructed image is conducted using the spiral phase Hilbert transform (HS). It has been tested using computer generated interferograms and real data. The performance of the presented SR-FABEMD-HS method is compared with other normalization techniques. Its superiority, potential and robustness to high fringe density variations and the presence of noise, modulation and background illumination defects in analyzed fringe patterns has been corroborated.

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Section: Introductionmentioning

confidence: 99%

Adaptive enhancement of optical fringe patterns by selective reconstruction using FABEMD algorithm and Hilbert spiral transform

2012

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“…Several normalization solutions have been proposed over the last decade. They include interferogram clipping [32,33], 2-D envelope transform coupled with spin filtering [73], two orthogonal bandpass filtering [74], the quadrature transform isotropic operator (Hilbert transform) algorithm [75], adaptive filtering as a linear combination of isotropic bandpass filtering [76], continuous wavelet transform (CWT) processing coupled with synthesized interferogram clipping [77], directional derivative approach [78], and the bidimensional empirical mode decomposition aided by the partial Hilbert transformation [51].…”

Section: Denoising Detrending and Normalizationmentioning

confidence: 99%

Hilbert-Huang processing and analysis of complex fringe patterns

2014

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Single-frame fringe pattern processing and analysis is an important task of optical interferometry, structural illumination and moiré techniques. In this contribution we present several algorithmic solutions based on the notion of Hilbert-Huang transform consisting of empirical mode decomposition algorithm and Hilbert spectral analysis. EMD adaptively dissects a meaningful number of intrinsic mode functions from the analyzed pattern. Appropriately managing this set of functions results in a powerful fringe processing tool. We describe in detail especially tailored manners proposed to extend the EMD algorithm to 2D and perform Hilbert-transform-aided efficient fringe pattern denoising, detrending and amplitude/phase demodulation.

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“…In contrast with a single polynomial, spline functions are more suitable for fitting the local deformations of a phase map [24][25][26]. In [28,41], and [42], other advanced methods for interpolating the fringe skeletons are suggested.…”

Section: Fringe Tracking Directed By Principal Vectorsmentioning

confidence: 99%

Principal-vector-directed fringe-tracking technique

Zhang

Guo

2014

Appl. Opt.

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Fringe tracking is one of the most straightforward techniques for analyzing a single fringe pattern. This work presents a principal-vector-directed fringe-tracking technique. It uses Gaussian derivatives for estimating fringe gradients and uses hysteresis thresholding for segmenting singular points, thus improving the principal component analysis method. Using it allows us to estimate the principal vectors of fringes from a pattern with high noise. The fringe-tracking procedure is directed by these principal vectors, so that erroneous results induced by noise and other error-inducing factors are avoided. At the same time, the singular point regions of the fringe pattern are identified automatically. Using them allows us to determine paths through which the "seed" point for each fringe skeleton is easy to find, thus alleviating the computational burden in processing the fringe pattern. The results of a numerical simulation and experiment demonstrate this method to be valid.

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Normalization and noise-reduction algorithm for fringe patterns

Cited by 36 publications

References 15 publications

Adaptive enhancement of optical fringe patterns by selective reconstruction using FABEMD algorithm and Hilbert spiral transform

Adaptive enhancement of optical fringe patterns by selective reconstruction using FABEMD algorithm and Hilbert spiral transform

Hilbert-Huang processing and analysis of complex fringe patterns

Principal-vector-directed fringe-tracking technique

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