Open AccessDisparity Computation Through PDE and Data-Driven CeNN Technique
Author(s) -
Mahima Lakra,
Sanjeev Kumar
Publication year - 2021
Publication title -
traitement du signal/ts. traitement du signal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.279
H-Index - 11
eISSN - 1958-5608
pISSN - 0765-0019
DOI - 10.18280/ts.380415
Subject(s) - classification of discontinuities , regularization (linguistics) , computation , partial differential equation , minification , mathematics , mathematical optimization , algorithm , scheme (mathematics) , term (time) , computer science , energy (signal processing) , artificial intelligence , mathematical analysis , statistics , physics , quantum mechanics
This paper proposes a variational approach by minimizing the energy functional to compute the disparity from a given pair of consecutive images. The partial differential equation (PDE) is modeled from the energy function to address the minimization problem. We incorporate a distance regularization term in the PDE model to preserve the boundaries' discontinuities. The proposed PDE is numerically solved by a cellular neural network (CeNN) algorithm. This CeNN based scheme is stable and consistent. The effectiveness of the proposed algorithm is shown by a detailed experimental study along with its superiority over some of the existing algorithms.