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

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

doi:10.1002/jemt.23162

Cited by 21 publications

(13 citation statements)

References 21 publications

(26 reference statements)

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“…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]. In order to provide better performance than modified Kalman filter, in terms of accuracy of state estimation, Lee et al proposed a new filtering method based on adaptive neural network, [22].…”

Section: Approximation Methodsmentioning

confidence: 99%

“…In SFF, when 2D images are obtained, mechanical vibrations occur in each step, also referred as jitter noise. The jitter noise is modeled as Lévy symmetric stable function for reflecting the real environment of SFF, [21], [36]- [38]. Lévy symmetric stable distribution has two parameters, one of which is stability index α and the other is scale parameter c. These parameters have the range of values as 0 < α ≤ 2, and c> 0.…”

Section: Noise Modelingmentioning

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%

See 2 more Smart Citations

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%

Section: Noise Modelingmentioning

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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“…Lévy distribution is often used as non-Gaussian function. In this article, we model Gaussian and Lévy distribution in a manner similar to that of two prior studies [17,18]. The Gaussian probability density function (PDF) is completely described by N(p,σp).…”

Section: Noise Modelingmentioning

confidence: 99%

“…Therefore, the results are not very accurate [16]. In the literature, various filters can be used for removing the jitter noise [17,18,19]. Recent research has applied Kalman filter to remove the jitter noise in SFF.…”

Section: Introductionmentioning

confidence: 99%

Jitter Elimination in Shape Recovery by using Adaptive Neural Network Filter

Lee

Jang

Lee

2019

Sensors

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Three-dimensional (3D) cameras are expensive because they employ additional charged coupled device sensors and optical elements, e.g., lasers or complicated scanning mirror systems. One passive optical method, shape from focus (SFF), provides an efficient low cost solution for 3D cameras. However, mechanical vibration of the SFF imaging system causes jitter noise along the optical axis, which makes it difficult to obtain accurate shape information of objects. In traditional methods, this error cannot be removed and increases as the estimation of the shape recovery progresses. Therefore, the final 3D shape may be inaccurate. We introduce an accurate depth estimation method using an adaptive neural network (ANN) filter to remove the jitter noise effects. Jitter noise is modeled by both Gaussian distribution and non-Gaussian distribution. Then, focus curves are modeled by quadratic functions. The ANN filter is designed as an optimal estimator restoring the original position of each frame of the input image sequence in the modeled jitter noise, as a pre-processing step before the initial depth map is obtained. The proposed method was evaluated using image sequences of both synthetic and real objects. Experimental results demonstrate that it is reasonably efficient and that its accuracy is comparable with that of existing systems.

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Shape from focus based on 3D structure tensor using optical microscopy

Mahmood

Lee

2019

Microscopy Res & Technique

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Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.

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

Cited by 21 publications

References 21 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

Jitter Elimination in Shape Recovery by using Adaptive Neural Network Filter

Shape from focus based on 3D structure tensor using optical microscopy

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