Real-time processing for shape-from-focus techniques

Humayun, Jawad; Malik, Aamir Saeed

doi:10.1007/s11554-012-0316-z

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…The defocused image has fewer high-frequency components than the in-focus image. Statistics-based operators compute some statistical values, such as the variance [20,21]. This assumes that the defocused image has a smaller variance component than the in-focused image.…”

Section: Introductionmentioning

confidence: 99%

Edge Bleeding Artifact Reduction for Shape from Focus in Microscopic 3D Sensing

Park,

Baek

2023

Sensors

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Shape from focus enables microscopic 3D sensing by combining it with a microscope system. However, edge bleeding artifacts of estimated depth easily occur in this environment. Therefore, this study analyzed artifacts and proposed a method to reduce edge bleeding artifacts. As a result of the analysis, the artifact factors are the depth of field of the lens, object texture, brightness difference between layers, and the slope of the object. Additionally, to reduce artifacts, a weighted focus measure value method was proposed based on the asymmetry of local brightness in artifacts. The proposed reduction method was evaluated through simulation and implementation. Edge bleeding artifact reduction rates of up to 60% were shown in various focus measure operators. The proposed method can be used with postprocessing algorithms and reduces edge bleeding artifacts.

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Section: Introductionmentioning

confidence: 99%

Edge Bleeding Artifact Reduction for Shape from Focus in Microscopic 3D Sensing

Park,

Baek

2023

Sensors

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“…Obtaining the 3D shape of an object from 2D images is a fundamental purpose of research in computer vision. Many 3D shape reconstruction methods, using various cues, have been proposed, which include focus, [1], [2], defocus, [3], [4], texture, [5], [6], stereo, [7], [8], and motion, [9], [10]. Three dimensional shape recovery methods based on focus are important due to their low computational cost and easy implementation.…”

Section: Introductionmentioning

confidence: 99%

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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“…Inferring three-dimensional (3D) shape of an object from two-dimensional (2D) images is a fundamental problem in computer vision applications. Many 3D shape recovery techniques have been proposed in literature [1][2][3][4][5]. The methods can be categorized into two categories based on the optical reflective model.…”

Section: Introductionmentioning

confidence: 99%

Sampling Based on Kalman Filter for Shape from Focus in the Presence of Noise

et al. 2019

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Recovering three-dimensional (3D) shape of an object from two-dimensional (2D) information is one of the major domains of computer vision applications. Shape from Focus (SFF) is a passive optical technique that reconstructs 3D shape of an object using 2D images with different focus settings. When a 2D image sequence is obtained with constant step size in SFF, mechanical vibrations, referred as jitter noise, occur in each step. Since the jitter noise changes the focus values of 2D images, it causes erroneous recovery of 3D shape. In this paper, a new filtering method for estimating optimal image positions is proposed. First, jitter noise is modeled as Gaussian or speckle function, secondly, the focus curves acquired by one of the focus measure operators are modeled as a quadratic function for application of the filter. Finally, Kalman filter as the proposed method is designed and applied for removing jitter noise. The proposed method is experimented by using image sequences of synthetic and real objects. The performance is evaluated through various metrics to show the effectiveness of the proposed method in terms of reconstruction accuracy and computational complexity. Root Mean Square Error (RMSE), correlation, Peak Signal-to-Noise Ratio (PSNR), and computational time of the proposed method are improved on average by about 48%, 11%, 15%, and 5691%, respectively, compared with conventional filtering methods.

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Real-time processing for shape-from-focus techniques

Cited by 8 publications

References 23 publications

Edge Bleeding Artifact Reduction for Shape from Focus in Microscopic 3D Sensing

Edge Bleeding Artifact Reduction for Shape from Focus in Microscopic 3D Sensing

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

Sampling Based on Kalman Filter for Shape from Focus in the Presence of Noise

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