Removal of jitter noise in 3D shape recovery from image focus by using Kalman filter

Jang, Hoon-Seok; Muhammad, Mannan Saeed; Choi, Tae‐Sun

doi:10.1002/jemt.22966

Cited by 19 publications

(23 citation statements)

References 10 publications

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“…Muhammad and Choi also proposed a focus curve fitting method based on Lorentzian-Cauchy function to improve the performance of conventional focus curve fitting methods, such as Gaussian interpolation method, [29]. Jang et al proposed the use of Kalman filter to remove Gaussian jitter noise, [18]. To reflect the real environment of SFF, Jang et al proposed utilization of modified Kalman filter to improve the performance of conventional Kalman filter in the presence of non-Gaussian jitter noise, [21].…”

Section: Approximation Methodsmentioning

confidence: 99%

“…Various filtering techniques have been utilized for removing jitter noise, [18]- [22]. Conventional filters are linear filtering methods using minimum mean square error (MMSE) criterion.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This jitter noise is different from image noise, and it changes focus values along the optical axis due to random vibrations, thus, degrading the performance of 3D shape recovery. Filtering methods, such as Kalman filter, [18], Bayes filter, [19], particle filter, [20], modified Kalman filter, [21], and adaptive neural network filter, [22], have been proposed for removing this type of jitter noise. Since the conventional filters use minimum mean squared error (MMSE) criterion as a cost function, they only capture the second-order statistics of the error signal.…”

Section: Introductionmentioning

confidence: 99%

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Removal of Non-Gaussian Jitter Noise for Shape From Focus Through Improved Maximum Correntropy Criterion Kalman Filter

2020

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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 twodimensional (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 Lévy 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. INDEX TERMS Focus curve, improved maximum correntropy criterion Kalman filter (IMCC-KF), jitter noise, non-Gaussian distribution, shape from focus (SFF).

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Section: Approximation Methodsmentioning

confidence: 99%

“…Various filtering techniques have been utilized for removing jitter noise, [18]- [22]. Conventional filters are linear filtering methods using minimum mean square error (MMSE) criterion.…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Removal of Non-Gaussian Jitter Noise for Shape From Focus Through Improved Maximum Correntropy Criterion Kalman Filter

2020

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“…An initial depth is obtained by maximizing the focus measure in the optical direction. Initial depth is further refined by applying a machine learning or approximation model (Jang, Muhammad, & Choi, 2018;Moeller, Benning, Schönlieb, & Cremers, 2015;Tseng & Wang, 2014).…”

Section: Introductionmentioning

confidence: 99%

Cross‐scale focus measure aggregation in depth recovery of microscopic objects

Mahmood

2019

Microscopy Res & Technique

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In shape from focus (SFF) methods, image focus volume enhancement is an important issue for acquiring accurate depth maps. Mostly, conventional approaches do focus aggregation locally to enhance the focus volume, which may not suppress noisy focus measurements properly and consequently, may provide deteriorated depth maps. Multiresolution fusion‐based method is proposed for image focus volume enhancement. First, an initial focus volume is obtained by applying a conventional focus measure. Then, a pyramid of focus volumes is computed using Gaussian filters and subsampling. Focus measures from various focus volumes at different levels are merged into a single resultant focus volume. Finally, the depth map is obtained from the resultant focus volume by maximizing the focus measure in the optical‐axis direction. According to the best of my knowledge, the cross‐scale aggregation has never been used in enhancing the image focus volume in SFF. The proposed method is evaluated through the experiments using image sequences of real microscopic and simulated objects. Results comparisons based on root mean square error (RMSE) and correlation demonstrate the effectiveness of the proposed method in improving the focus volume and depth map. The proposed fusion method of volumes is a simple but effective. The idea of cross‐scale aggregation of focus measures is effective in providing precise focus measures that consequently, provide accurate depth map. In future work, it will further be explored and a more sophisticated and optimization‐based fusion algorithm will be applied.

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“…Since the focus values of the obtained 2D images are changed due to jitter noise, erroneous 3D shape recovery results are acquired. To solve this problem, a filtering technique which searches the best focused frame in each pixel by using Kalman filter has been proposed (Jang, Muhammad, & Choi, ). However, this technique has a limitation in noise modeling.…”

Section: Introductionmentioning

confidence: 99%

Optimal depth estimation using modified Kalman filter in the presence of non‐Gaussian jitter noise

Jang

Muhammad

Choi

2018

Microscopy Res & Technique

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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.

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Removal of jitter noise in 3D shape recovery from image focus by using Kalman filter

Cited by 19 publications

References 10 publications

Removal of Non-Gaussian Jitter Noise for Shape From Focus Through Improved Maximum Correntropy Criterion Kalman Filter

Removal of Non-Gaussian Jitter Noise for Shape From Focus Through Improved Maximum Correntropy Criterion Kalman Filter

Cross‐scale focus measure aggregation in depth recovery of microscopic objects

Optimal depth estimation using modified Kalman filter in the presence of non‐Gaussian jitter noise

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