Removal of non-Gaussian jitter noise for shape from focus through improved maximum correntropy criterion Kalman filter

Title
Removal of non-Gaussian jitter noise for shape from focus through improved maximum correntropy criterion Kalman filter
Authors
강민구장훈석Mannan Saeed Muhammad
Keywords
Focus curve; jitter noise; shape from focus
Issue Date
2020-02
Publisher
IEEE Access
Citation
VOL 8-36255
Abstract
Three-dimensional (3D) shape reconstruction from one or multiple observations is a primary problem of computer vision. Shape from Focus (SFF) is a passive optical method that uses multiple two-dimensional (2D) images with different focus levels. When obtaining 2D images in each step along the optical axis, mechanical vibrations, referred as jitter noise, occur. SFF techniques are vulnerable to jitter noise that can vary focus values in 2D images. In this paper, new filtering method, which provides high accuracy of 3D shape reconstruction and low computational cost, is proposed. First, jitter noise is modeled as Levy distribution. This assumption makes it possible to show the influence of proposed filtering method in real environment with non-Gaussian jitter noise. Second, focus curves are modeled as Gaussian function to compare the performance of proposed filtering method with those of the conventional filtering methods. Finally, improved maximum correntropy criterion Kalman filter (IMCC-KF) is designed as a post-processing step, and is applied to the modeled focus curves. The experiments are performed on real and synthetic objects and the results demonstrate the effectiveness of proposed method.
URI
http://pubs.kist.re.kr/handle/201004/71170
ISSN
2169-3536
Appears in Collections:
KIST Publication > Article
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