2020-03-262020-03-2620192019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings9781728114910https://hdl.handle.net/20.500.12585/9155The 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.Recurso electrónicoapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObject10.1109/STSIVA.2019.87302283-D ReconstructionDictionary LearningImage restorationPhase unwrappingSparse representationGaussian noise (electronic)Optical data processingRestorationVisionWhite noise3D reconstructionAdditive White Gaussian noiseDictionary learningFringe projection profilometryImage processing applicationsPhase unwrappingSparse representationWrapped phase mapImage reconstructioninfo:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 InternacionalUniversidad Tecnológica de BolívarRepositorio UTB5719227001657204065355572095421953614215630024329839300