An Efficient Neural Network for Shape from Focus with Weight Passing Method

Kim, Hyo‐Jong; Mahmood, Muhammad Tariq; Choi, Tae‐Sun

doi:10.3390/app8091648

Cited by 14 publications

(4 citation statements)

References 32 publications

(59 reference statements)

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“…Kim et al suggest an efficient neural network model for shape from focus along with a weight passing method. The proposed network reduces the computational complexity with accuracy comparable with the conventional model [15]. Yang et al propose absolute phase retrieval using only one coded pattern together with geometric constraints in a fringe projection system.…”

Section: -D Imagingmentioning

confidence: 99%

Editorial on Special Issue “Holography, 3-D Imaging and 3-D Display”

et al. 2020

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Section: -D Imagingmentioning

confidence: 99%

Editorial on Special Issue “Holography, 3-D Imaging and 3-D Display”

et al. 2020

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“…By using these methods to identify all clear pixel positions, discrete 3D shapes can be reconstructed. In addition, many other types of shape-fromfocus algorithms have emerged, such as focus surface fitting algorithms [10] [11] and optimization-based algorithms [12] [13].…”

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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“…Lastly, if positions of the sharpest pixels on each image, in the sequence, are known, it is possible to combine them into an initial depth map. For more accurate shape reconstruction any approximation technique of SFF can be used, as a final step for 3D shape reconstruction (Muhammad and Choi (2010); Kim, Mahmood, and Choi (2018)).…”

Section: Introductionmentioning

confidence: 99%

“…Asif and Choi (2001) proposed a method using neural networks to optimize the time complexity of SFF with a focused image surface Their method maximizes the focus measure.value by utilizing a two‐layer neural network with sigmoidal neurons as an activation function. Kim et al (2018) further enhanced this method by improving the efficiency of neural networks by introducing a weight passing method. Muhammad and Choi (2010) proposed the SFF method based on simple FM and Bezier Surface approximation.…”

Section: Introductionmentioning

confidence: 99%

A simplified approach using deep neural network for fast and accurate shape from focus

Mutahira

Muhammad

et al. 2020

Microscopy Res & Technique

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Three-dimensional shape recovery is an important issue in the field of computer vision. Shape from Focus (SFF) is one of the passive techniques that uses focus information to estimate the three-dimensional shape of an object in the scene. Images are taken at multiple positions along the optical axis of the imaging device and are stored in a stack. In order to reconstruct the three dimensional shape of the object, the best-focused positions are acquired by maximizing the focus curves obtained via application of a focus measure operator. In this article, Deep Neural Network (DNN) is employed to extract the more accurate depth of each object point in the image stack. The size of each image in the stack is first reduced and then provided to the proposed DNN network to aggregate the shape. The initial shape is refined by applying a median filter, and later the reconstructed shape is sized back to original by utilizing bi-linear interpolation. The results are compared with commonly used focus measure operators by employing root mean squared error (RMSE), correlation, and image quality index (Q). Compared to other methods, the proposed SFF method using DNN shows higher precision and low computational time consumption.

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An Efficient Neural Network for Shape from Focus with Weight Passing Method

Cited by 14 publications

References 32 publications

Editorial on Special Issue “Holography, 3-D Imaging and 3-D Display”

Editorial on Special Issue “Holography, 3-D Imaging and 3-D Display”

Wafer Surface Reconstruction Based on Shape From Focus

A simplified approach using deep neural network for fast and accurate shape from focus

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