Book ChapterDOI

# Effective Denoising with Non-local Means Filter for Reliable Unwrapping of Digital Holographic Interferometric Fringes

P. L. Aparna
01 Jan 2018-pp 13-24

TL;DR: This paper proposes a preprocessing technique that removes the noise from the interference field, thereby improving the performance of naive unwrapping algorithms and validates the applicability of proposed approach for processing the noisy interference field.

AbstractEstimation of phase from the complex interference field has become an emerging area of research for last few decades. The phase values obtained by using arctan function are limited to the interval $$(-\pi , \pi ]$$. Such phase map is known as wrapped phase. The unwrapping process, which produces continuous phase map from the wrapped phase, becomes tedious in presence of noise. In this paper, we propose a preprocessing technique that removes the noise from the interference field, thereby improving the performance of naive unwrapping algorithms. For de-noising of the complex field, real part and imaginary parts of the field are processed separately. Real-valued images (real and imaginary parts) are processed using non-local means filter with non-Euclidian distance measure. The de-noised real and imaginary parts are then combined to form a clean interference field. MATLAB’s unwrap function is used as unwrapping algorithm to get the continuous phase from the cleaned interference field. Comparison with the Frost’s filter validates the applicability of proposed approach for processing the noisy interference field.

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TL;DR: In this article, a fast Fourier transform method of topography and interferometry is proposed to discriminate between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour generation techniques.
Abstract: A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

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##### References
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Journal Article
TL;DR: In this article, a fast Fourier transform method of topography and interferometry is proposed to discriminate between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour generation techniques.
Abstract: A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

3,742 citations

Journal ArticleDOI
TL;DR: In this paper, a fast Fourier transform method of topography and interferometry is proposed to discriminate between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour generation techniques.
Abstract: A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

3,417 citations

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TL;DR: Experimental results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.
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TL;DR: In this paper, an approach to 'unwrapping' the 2 pi ambiguities in the two-dimensional data set is presented, where it is found that noise and geometrical radar layover corrupt measurements locally, and these local errors can propagate to form global phase errors that affect the entire image.
Abstract: Interferometric synthetic aperture radar observations provide a means for obtaining high-resolution digital topographic maps from measurements of amplitude and phase of two complex radar images. The phase of the radar echoes may only be measured modulo 2 pi; however, the whole phase at each point in the image is needed to obtain elevations. An approach to 'unwrapping' the 2 pi ambiguities in the two-dimensional data set is presented. It is found that noise and geometrical radar layover corrupt measurements locally, and these local errors can propagate to form global phase errors that affect the entire image. It is shown that the local errors, or residues, can be readily identified and avoided in the global phase estimation. A rectified digital topographic map derived from the unwrapped phase values is presented.

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Abstract: Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.

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