In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance, in terms of high accuracy and edge-preserving, over two representative single-frame techniques: Fourier transform profilometry and windowed Fourier transform profilometry.

/pdf/fringe-pattern-analysis-using-deep-learning-2sxrfq04t9.pdf

Fringe pattern analysis using deep learning

We present a digital signal processing technique that reduces the speckle content in reconstructed digital holograms. The method is based on sequential sampling of the discrete Fourier transform of the reconstructed image field. Speckle reduction is achieved at the expense of a reduced intensity and resolution, but this trade-off is shown to be greatly superior to that imposed by the traditional mean and median filtering techniques. In particular, we show that the speckle can be reduced by half with no loss of resolution (according to standard definitions of both metrics).

http://www.cs.may.ie/~tnaughton/pubs/varasto/MHMFCJN2007p.pdf

Reduction of speckle in digital holography by discrete Fourier filtering.

Purpose To develop a technique to quantify biomechanical changes in the cornea after microkeratome incisions as would be performed in laser in situ keratomileusis. Setting St Thomas' Hospital, London, and the Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, United Kingdom. Methods Corneal displacements of whole sheep eyes were studied under hydrostatic loading using electronic speckle pattern interferometry before and after microkeratome incisions. Results After hydrostatic loading, there was a 20.7% increase in corneal displacement in corneas with microkeratome incisions compared to unoperated corneas; this was statistically significant ( P  = .0068, unpaired t test). Conclusions Results show that in the formation of the microkeratome flap, collagen fibers are severed and minimal biomechanical loading is distributed through the flap. Corneal biomechanical integrity is compromised after microkeratome incisions.

Interferometric technique to measure biomechanical changes in the cornea induced by refractive surgery.

In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, here, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance in terms of high accuracy and edge-preserving over two representative single-frame techniques: Fourier transform profilometry and Windowed Fourier profilometry.

Two-wavelength micro-interferometry for 3-D surface profiling

From the Publisher:
Digital Speckle Pattern Interferometry (DSPI) is the generic name for a class of important interferometric techniques such as TV holography, electronic holography or phase shifting pattern interferometry, all of which involve similar optical and electronic principles. These techniques are increasingly used in a wide range of fields including experimental mechanics, vibration analysis and non-destructive testing. 
Digital Speckle Interferometry and Related Techniques provides a single source of information in this rapidly progressing field. Containing contributions from leading experts, it provides the key background information, including the fundamental concepts, techniques, and applications, and presents the major technological progress that has contributed to revitalization in the field over the past fifteen years, including digital speckle photography and digital holographic interferometry. 
This is an invaluable text for practising engineers in industry and research institutes, and academic and development researchers in scientific and engineering disciplines who use, or are interested in, DSPI. This title would also be of interest to teachers and final year undergraduate/postsgraduate students of physics, optics, experiemental mechanics, photomechanics, optical metrology, engineering metrology, and non-destructive testing.

P. K. Rastogi

Papers

Digital Speckle Pattern Interferometry & Related Techniques