Abstract
Fringe visibility and noise removal, are key success factors in interferometric techniques, where novel deep learning techniques can be applied. We test the use U-Net deep convolutional network applied to the obtained interference images, trained with an ad-hoc generated image dataset with complex fringe patterns, computed using high order Zernike polynomials.
Highlights
Interferometric techniques have been used along years as one of the most important methods for non-contact measurements and quality assessment of wavefronts coming from objects surfaces, but one of the most relevant caveats for accurate measurements is the noise level presented in the obtained interferograms
For the speckle pattern interferometry, the issue coming from the background noise and the need to increase fringe visibility are key factors to the success of the techniques, making highly complex the increase of fringe visibility to facilitate the image interpretation in real time
The U-Net architecture is a convolutional encoder – decoder with internal connections between encoder and decoder paths with a well-proven performance when it is applied to denoise problems
Summary
Interferometric techniques have been used along years as one of the most important methods for non-contact measurements and quality assessment of wavefronts coming from objects surfaces, but one of the most relevant caveats for accurate measurements is the noise level presented in the obtained interferograms. To deal with this caveat image filtering techniques have been applied to remove the noise (specially speckle or salt & pepper noise) from the interferograms, using more or less complex filters based on Fourier transforms[1], or other filters for high frequency noise in the spatial domain [2]. For the speckle pattern interferometry, the issue coming from the background noise and the need to increase fringe visibility are key factors to the success of the techniques, making highly complex the increase of fringe visibility to facilitate the image interpretation in real time
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