Jitter Elimination in Shape Recovery by using Adaptive Neural Network Filter

Lee, Sung-An; Jang, Hoon-Seok; Lee, Byung-Geun

doi:10.3390/s19112566

Cited by 15 publications

(5 citation statements)

References 23 publications

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“…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]. Chen et al proposed a 3D shape reconstruction method using maximum correntropy Kalman filter, [27], where a maximum correntropy Kalman filter is utilized for removing the noise related to the surface texture of the object.…”

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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“…Jang et al in [28] proposed the removal of Jitter noise using Kalman Filter. Since then, many variants of their method have been proposed [29]- [33]. However, all of their methods used scalar-models for Kalman filter (i.e., the system matrix was taken as 1), and ignored the dynamic nature of focus cues.…”

Section: Motivationmentioning

confidence: 99%

Sampling-Noise Modeling & Removal in Shape From Focus Systems Through Kalman Filter

et al. 2021

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Shape from Focus (SFF) is one of the passive techniques to recover the shape of an object under consideration. It utilizes the focus cue present in the stack of images, obtained by a single camera. In SFF when the images are acquired, the inter-frame distance, also known as the sampling step size, is assumed to be constant. However, in practice, due to mechanical constraints, sampling step size cannot remain constant. The inconsistency in the sampling step size causes the problem of jitter, and produces Jitter noise in focus curves. This Jitter noise is not visible in images, because each pixel in an image (of the stack) will be subjected to the same error in focus. Thus, traditional image denoising techniques will not work. This paper formulates a model of the Jitter noise, followed by the design of system and measurement models for Kalman filter. Then, the jittering problem for SFF systems is solved using the proposed filtering technique. Experiments are performed on simulated and real objects. Ten noise levels are considered for simulated, and four for real objects. RMSE and Correlation are used to measure the reconstructed shape. The results show the effectiveness of the proposed scheme.

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“…The images of the slices are obtained by the camera to construct a focused image sequence. Then, the focus value of the image sequence is analyzed, and the maximum focus value is obtained in the optical axis direction to obtain the depth [4]. This method balances the convenience and accuracy of measurement.…”

Section: Introductionmentioning

confidence: 99%

Wafer Surface Reconstruction Based on Shape From Focus

Wang,

Chen,

Shi

et al. 2024

IEEE Access

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Scanning electron microscope, atomic force microscope and other equipment play an important role in the fields of topography restoration and detection. However, these devices are generally used in nanometer-scale measurement scenarios. For wafer topography quality control scenarios ranging from microns to hundreds of microns, these technologies have problems such as high cost and slow detection speed. Therefore, developing new, low-cost, and high-precision methods is necessary. To address this problem, a wafer surface reconstruction framework is proposed based on the shape-from-focus principle. In view of the characteristics of the large area and micro-small height of the wafer, to solve the limitations of the existing shape from focus framework, which is generally based on a single field, we created a multi-field image sequence rapid acquisition system and proposed the use of pulse control methods to achieve rapid acquisition of large area images. On the other hand, this paper proposes a dual filtering framework combining the Levy flight filtering principle with the SOR algorithm in point cloud filtering to achieve a balance between smoothing the depth map and maintaining the detailed structure, reducing the impact of noise, and improving the morphology restoration accuracy. To avoid splicing seams between fields, the progressive detection multifield stitching technique is used to complete large-area depth data stitching. Experiments were conducted on both synthetic and real objects to verify the effectiveness of the proposed method. In terms of synthesized images, the accuracy of the three methods significantly improved after applying the proposed method framework. After applying the Tenenbaum method framework, its correlation and peak signal-tonoise ratio improved by 7.5% and 38.2%, respectively, and its root mean square error was reduced by 40.7%. The excellent accuracy reconstruction results of the proposed method was verified through accuracy evaluation experiments. The height errors of the three methods used were all higher than 1 μm. However, after using the proposed method framework, the maximum error was only 0.24 μm. The experimental results indicated that this method overcomes the area limitation of traditional SFF and is suitable for applying wafer surface morphology measurements.

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Jitter Elimination in Shape Recovery by using Adaptive Neural Network Filter

Cited by 15 publications

References 23 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

Sampling-Noise Modeling & Removal in Shape From Focus Systems Through Kalman Filter

Wafer Surface Reconstruction Based on Shape From Focus

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