Computer Science ›› 2015, Vol. 42 ›› Issue (7): 314-319.doi: 10.11896/j.issn.1002-137X.2015.07.067

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Dense Depth Map Reconstruction via Image Guided Second-order Total Generalized Variation

WU Shao-qun YUAN Hong-xing AN Peng CHENG Pei-hong   

  • Online:2018-11-14 Published:2018-11-14

Abstract: The depth map reconstruction using image colors may recovery the depth discontinuities at object boundaries,but will damage depth uniformities inside objects.In order to solve this problem,we formulated depth reconstruction as a convex optimization problem which is regularized by image guided total generalized variation.By incorporating image diffusion tensor into the variation regularizer,the proposed method generates piecewise smooth depth while preserving discontinuities at object boundaries.To efficiently solve the problem,a first-order primal-dual scheme was derived based on the Legendre-Fenchel transformation.Experimental results demonstrate that our method can preserve depth discontinuities at object boundaries and uniformities inside objects and outperform existing methods in terms of peak signal-to-noise ratio,normalized cross-covariance and mean absolute error.

Key words: Depth map reconstruction,Depth discontinuities,Depth uniformities,Total generalized variation

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