Rapid microscope auto‐focus method for uneven surfaces based on image fusion

Jiang, Mawei; Yang, Ting; Xiaoli, Xu; Zhang, Xue‐dian; Li, Feng

doi:10.1002/jemt.23328

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…Six objective representative evaluation metrics were selected in this section, namely SD (calculated by Equations (6) and (7)), SSIM (obtained by Equations (8) and (9)), SF (obtained by Equation (11)), PSNR (calculated by Equations (13) and (14)), Q abf (obtained by Equations (15) and (16)) and AG (displayed in Equation (17)). 6,15 Among them, SD represents the gray-level distribution and contrast of the fused image; SSIM measures the structural similarity between the series of multi-focus nonwoven images and the fused image, thereby evaluating the amount of preserved structural information in the fused image; SF reflects the rate of change of the gray image level, where a higher gray value indicates a clearer fused image; PSNR is used to measure the difference between images; Q abf can assess the amount of edge information preserved in the fused image; and AG reflects the characteristics of the texture transformation. Standard deviation, SD …”

Section: Resultsmentioning

confidence: 99%

“…5 Getting a clear image that contains all relevant objects in one area is the prerequisite for achieving high-precision optical measurements. 6 However, during the image acquisition process of nonwoven fabrics, partially focused images are often obtained instead of completely clear images, since the thickness of the nonwoven material exceeds the depth of field of the microscope. 7 Multi-focus image fusion (MFF) is an effective technique to reconstruct a fully focused image based on a series of partially focused images of the same scene, thus permitting the accurate measurement of object features.…”

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confidence: 99%

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confidence: 99%

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Spectral- and spatial-based multi-focus image fusion method towards multi-layer nonwovens

Zhu

Sun

et al. 2023

Textile Research Journal

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In the microscopic imaging scenario where the object thickness exceeds the depth of field of the microscope, multi-focus image fusion (MFF) is an effective method to generate an all-in-focus image. However, for nonwoven fabric for which the captured image number is up to 100 or more, the existing methods often underperform in areas near the fiber edges, owing to image ghosting and noise accumulation caused by the platform moving. To address the above problem, this paper presents a method designed to fuse multi-layer micro-images based on the combination of spectral and spatial features of the images. Firstly, the spectral domain-based map is generated by decomposition and reconstruction of the high-frequency and low-frequency components of the images, aimed at obtaining the edge information. Simultaneously, the spatial domain-based fuse map is built through sharpness measurement, referring to visual perception. Finally, the two methods are combined via an optimized weight to obtain an all-in-focus fused image. Four groups of real-world data consisting of 100 multi-focus nonwoven images are utilized to verify the superiority of this method. The experimental results demonstrate that the proposed method can obtain satisfactory performance in terms of both human visual evaluation and objective evaluation compared with the image fusion framework based on the convolutional neural network, MFF, region-based image fusion algorithm and convolutional neural network state-of-the-art fusion methods.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Spectral- and spatial-based multi-focus image fusion method towards multi-layer nonwovens

Zhu

Sun

et al. 2023

Textile Research Journal

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“…These approaches can be categorized into the following groups based on the principles applied. 1) Analyzing images acquired using software algorithm to determine the best focus [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]; 2) based on auxiliary laser focusing spot deviation or spot diameter variation [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Study of Microscope Autofocus Based on Weighted Light Intensity

Lu¹,

Tu²,

Wu³

et al. 2021

AJPA

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Microscopes auto focusing is critically important for a great spectrum of scientific and industrial applications.Finding the best focus position for variety of surfaces under measurement proved to be challenging. In order to provide a rapid and accurate method for microscope focusing positioning, a laser focus deviate system was employed for microscope focusing position. The relationship between focusing sensitivity and the surface under measurement was investigated. First, the laser focus deviate technique is introduced in light of characteristic parameters such as the diameter of the focused spot, the laser wavelength and the distance from the optimal focus. Then, the focusing displacement versus spot position curve fitting is performed for a smooth isotropic surface to directly locate the best focusing position. Following, it is analyzed that when applied to anisotropic and rough surfaces, the centroid of the image spot after reflection/scattering from the surface will deviate from its original light intensity distribution, thus deviate from the calculated optimal focus position and cause focusing errors. Finally, a criterion of weighted light intensity value Q is introduced to achieve fast focusing of anisotropic surfaces in combination with the laser focusing deviate technique. The experimental results show that for a 20× microscope objective system, the focusing accuracy of this focusing positioning method is 1.7 µm on smooth surfaces; and is at the micro meter level for rough surfaces. It basically meets the requirements for rapid, stable and reliable microscope auto focusing needs.

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“…In addition, ultrasonic methods cannot auto-focus accurately on objects behind the glass [12], thus limiting its usage scenarios. Compared to the conventional AFTs, auto-focusing methods based on digital image processing (DIP-AF) have been found to have the advantages of integrated, miniaturized, and low-cost applications [13], such as digital camera, video camera, and microscope imaging [14,15]. However, little attention has been paid to AFTs with telescope systems, which are critical for capturing large amounts of image information through high-resolution CCDs in applications of astronomy observations [16,17].…”

Section: Introductionmentioning

confidence: 99%

Accurate and Rapid Auto-Focus Methods Based on Image Quality Assessment for Telescope Observation

Yang

Chen

Zhou³

et al. 2020

Applied Sciences

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Aiming at improving the speed and accuracy of auto-focus for telescope observation, algorithms for image estimation and auto-focus were investigated and are discussed in this article. Based on the image quality assessment, the auto-focusing process of the telescope system is realized by using the mountain-climb search method. Several evaluation functions were tested in different scenarios. It is demonstrated that the Tenengrad image estimation function (IEF) is suitable for an instant and accurate auto-focus process of the telescope. Furthermore, we implemented sampling and dynamic adaptive focusing window (ES-DAFW) methods with the Tenengrad IEF to enhance the sensitivity and accuracy of the auto-focus process. The experimental results showed that our ES-DATW method can provide more accurate results in less time for the auto-focus process compared to the conventional approaches, especially for a sparse image. These results promise significant applications to the auto-focusing of other telescopes with image quality assessment.

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Rapid microscope auto‐focus method for uneven surfaces based on image fusion

Cited by 4 publications

References 18 publications

Spectral- and spatial-based multi-focus image fusion method towards multi-layer nonwovens

Spectral- and spatial-based multi-focus image fusion method towards multi-layer nonwovens

Study of Microscope Autofocus Based on Weighted Light Intensity

Accurate and Rapid Auto-Focus Methods Based on Image Quality Assessment for Telescope Observation

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