Evaluation of focus measures for the autofocus of line scan cameras

Xia, Xiaohua; Ya, Yao; Jia, Liang; Fang, Suping; Yang, Zhiyong; Cui, Dan

doi:10.1016/j.ijleo.2016.05.114

Cited by 31 publications

(7 citation statements)

References 26 publications

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“…Columns one to four in the figure are the three models of the 3D morphologies that were recovered and generated by using 3 × 3, 5 × 5, and 7 × 7 windows and the adaptive evaluation window proposed in Salt-and-pepper noise with a density of 0.01 was added to the images of the 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, 90th, and 100th frames of the spherical, complex, and simple surface models. The three focus measures F SML , F TEN [38], and F GLV [39] were selected as the test measures. Windows with a size of 3 × 3, 5 × 5, 7 × 7, and the adaptive evaluation window proposed in this paper were used to conduct a 3D morphological recovery test for the image sequences generated using the three models.…”

Section: Test Results and Analysismentioning

confidence: 99%

Volumetric Tooth Wear Measurement of Scraper Conveyor Sprocket Using Shape from Focus-Based Method

Ding

Liu

2019

Applied Sciences

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Volumetric tooth wear measurement is important to assess the life of scraper conveyor sprocket. A shape from focus-based method is used to measure scraper conveyor sprocket tooth wear. This method reduces the complexity of the process and improves the accuracy and efficiency of existing methods. A prototype set of sequence images taken by the camera facing the sprocket teeth is collected by controlling the fabricated track movement. In this method, a normal distribution operator image filtering is employed to improve the accuracy of an evaluation function value calculation. In order to detect noisy pixels, a normal operator is used, which involves with using a median filter to retain as much of the original image information as possible. In addition, an adaptive evaluation window selection method is proposed to address the difficulty associated with identifying an appropriate evaluation window to calculate the focused evaluation value. The shape and size of the evaluation window are autonomously determined using the correlation value of the grey scale co-occurrence matrix generated from the measured pixels' neighbourhood pixels. A reverse engineering technique is used to quantitatively verify the shape volume recovery accuracy of different evaluation windows. The test results demonstrate that the proposed method can effectively measure sprocket teeth wear volume with an accuracy up to 97.23%. discussed the wear condition of a driving sprocket and the influence of wear on the sliding distance by taking the sliding speed and sliding distance of the meshing process as the index. Wang et al. [8] also analyzed the relationship between the deformation of a ring chain and driving sprocket wear by combining numerical analysis with experiments. However, these methods are not only tedious and time-consuming, they are also not sufficiently accurate or efficient.The research of computer vision in industry field has attracted more and more attention of many researchers. Alverdi et al. [9] proposed a new way of using images to model the kerf profile in abrasive water jet milling. Qian et al. [10] presented an algorithm to compute the axis and generatrix focus on complex surfaces or irregular surfaces. A new monitoring technique for burr detection was proposed for the optimization of drill geometry and process parameters [11]. In addition, as a relatively simple and practical 3D reconstruction technology, shape from focus (SFF) has been applied to tool wear measurements [12,13], LCD/TFT (Liquid Crystal Display/Thin-Film Technology) display manufacturing [14] and grinding wheel surface morphology [15], etc.To realize 3D surface topography restoration, in 1988, Darrell et al. [16] proposed using a Laplace operator-Gauss fitting method to search the clear frame of pixels in the sequence partial focus image according to image focusing information. In the 1990s, Nayar et al. [17,18] proposed an SFF-based method and obtained the height information of the corresponding surface of the window image by searching the image position cor...

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Section: Test Results and Analysismentioning

confidence: 99%

Volumetric Tooth Wear Measurement of Scraper Conveyor Sprocket Using Shape from Focus-Based Method

Ding

Liu

2019

Applied Sciences

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“…The Tenengrad operator [11,12] is a kind of discrete first-order difference operator, which is often used to evaluate the sharpness of an image. The Focus Measure (FM) of each pixel in each multi-focused image can be calculated as Equation (1):…”

Section: Methods 21 3d Information Extraction From Multi-focused Imag...mentioning

confidence: 99%

A 3D scene acquisition and reconstruction system via optical axial scanning

Dong

Liu

et al. 2023

Third Optics Frontier Conference (OFS 2023)

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Over the past few years, 3D display technology has improved so much that it is being used in many fields, such as education, medicine and the military. 3D holographic display is seen as the ultimate solution to 3D display, but one of the problems is that there is not enough 3D content available. Conventional methods to obtain 3D content from real scenes using lightfield cameras or RGB-D cameras are complicated. Here, we proposed a 3D scene acquisition and reconstruction system based on optical axial scanning. First an electrically tunable lens (ETL) was used for high-speed focus shift (up to 2.5 ms). A CCD camera is synchronized with the ETL to acquire multi-focused image sequence of real scene. Then, The Tenengrad operator was used to obtain the focusing area of each multi-focused image, and the 3D image were obtained. Finally, the Computer-Generated Hologram (CGH) can be obtained by the layer-based diffraction algorithm. The CGH was loaded onto the space light modulator (SLM) to reconstruct the 3D holographic image. The experimental results verify the feasibility of the system. This method will expand the application of 3D holographic display in the field of education, advertising, entertainment, and other fields.

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“…In terms of the image sharpness evaluation, Xia et al [ 4 ] have summarized and compared 16 traditional functions, including Laplace, Tenengrad, spatial frequency, wavelet transform, and fast Fourier transform, finding that Tenengrad performs best in the focus measure both for global search and local search, but has a weak noise immunity performance. In addition, Xia [ 5 ] proposed a new fusion algorithm by dividing information entropy and Tenengrad to enhance noise resistance, but it takes a bit longer timewise.…”

Section: Related Workmentioning

confidence: 99%

A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains

Zhou

et al. 2022

Sensors

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An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope’s image autofocus. First, images are decomposed with the NSST algorithm; then, the decomposed sub-band images are subjected to variance to obtain the energy of the sub-band coefficients; and finally, the evaluation value is obtained from the ratio of the energy of the high- and low-frequency sub-band coefficients. The experimental results show that the proposed algorithm delivers better noise immunity performance than other methods reviewed by this study while maintaining high sensitivity.

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Evaluation of focus measures for the autofocus of line scan cameras

Cited by 31 publications

References 26 publications

Volumetric Tooth Wear Measurement of Scraper Conveyor Sprocket Using Shape from Focus-Based Method

Volumetric Tooth Wear Measurement of Scraper Conveyor Sprocket Using Shape from Focus-Based Method

A 3D scene acquisition and reconstruction system via optical axial scanning

A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains

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