Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning
Universidad Tecnológica de Bolívar
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The problem of phase unwrapping from a noisy and also incomplete wrapped phase map arises in many optics and image processing applications. In this work, we propose a noise-robust approach for processing regional phase dislocations. Our approach combines phase unwrapping and sparse-based inpainting with dictionary learning to recover the continuous phase map. The method is validated both using numerically simulated data with strong additive white Gaussian noise and phase dislocations; and experimental data from fringe projection profilometry. Comparisons with other phase inpainting method referred to as PULSI+INTERP, show the suitability of the proposed method for phase restoration even in extremely noisy phases. The error given by the proposed method on the highest level of noise (RMSE=0.0269 Rad) remains the smallest compared to the error given by PULSI+INTERP for noise-free data (RMSE=0.0332 Rad). © 2019 IEEE.
This work has been partly funded by Colciencias project 538871552485, and by Universidad Tecnológica de Bolívar projects C2018P005 and C2018P018. J. Pineda and J. Meza thank Universidad Tecnológica de Bolívar for a Masters degree scholarship.
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