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Optimal depth estimation using modified Kalman filter in the presence of non‐Gaussian jitter noise
Author(s) -
Jang HoonSeok,
Muhammad Mannan Saeed,
Choi TaeSun
Publication year - 2019
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.23162
Subject(s) - jitter , noise (video) , kalman filter , gaussian noise , focus (optics) , computer science , gaussian , fast kalman filter , control theory (sociology) , gaussian filter , noise measurement , filter (signal processing) , extended kalman filter , algorithm , acoustics , computer vision , artificial intelligence , noise reduction , image (mathematics) , physics , optics , telecommunications , control (management) , quantum mechanics
The consideration of the noise that affects 3D shape recovery is becoming very important for accurate shape reconstruction. In Shape from Focus, when 2D image sequences are obtained, mechanical vibrations, referred as jitter noise, occur randomly along the z‐ axis, in each step. To model the noise for real world scenarios, this article uses Lévy distribution for noise profile modeling. Next, focus curves acquired by one of focus measure operators are modeled as Gaussian function to consider the effects of the jitter noise. Finally, since conventional Kalman filter provides good output under Gaussian noise only, a modified Kalman filter, as proposed method, is used to remove the jitter noise. Experiments are carried out using synthetic and real objects to show the effectiveness of the proposed method.