Automatic Compensation for Defects of Laser Reflective Patterns in Optics-Based Auto-Focusing Microscopes

Hsu, Wei‐Yen

doi:10.1109/jsen.2019.2949033

Cited by 17 publications

(3 citation statements)

References 27 publications

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“…However, in the real case, the captured laser spot image is likely an ellipse and not an ideal semicircular geometry. The ellipse shape may be caused by assembly errors of the instrument, variable reflection characteristics, or a tilt of the sample, and so on [40]. Consequently, an ellipse spot compensation algorithm is presented in this study, which uses the boundary information of the original laser spot image captured by CCD 1 to efficiently overcome this issue.…”

Section: Proposed Algorithm 31 Proposed Ellipse Spot Compensation Alg...mentioning

confidence: 99%

Innovative Image Processing Method to Improve Autofocusing Accuracy

Liu

2022

Sensors

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For automated optical inspection, autofocusing microscopes play an important role in capturing clear images of the measured object. At present, the image processing part of optics-based autofocusing microscopes often has various factors, which makes it impossible to describe the image information of the semicircular (or elliptical) spot with a simple circle-finding method. Accordingly, this study has developed a novel algorithm that can quickly calculate the ideal center of the elliptical spot and effectively compensate the linearity of the focusing characteristic curve. A prototype model was used to characterize and verify the proposed algorithm. The experimental results show that by using the proposed algorithm, the autofocusing accuracy can be effectively improved to less than 1.5 μm.

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Section: Proposed Algorithm 31 Proposed Ellipse Spot Compensation Alg...mentioning

confidence: 99%

Innovative Image Processing Method to Improve Autofocusing Accuracy

Liu

2022

Sensors

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“…The computational kernel of CEF is generally divided into two types: pixel distribution and transform domain [18, 19]. The former deals with the pixel values directly, while the latter deals with the information after the domain transformation with a huge drain on time [18], such as wavelet transform [20], frequency domain [21], power spectrum [22], etc. Gradient is the most direct and effective method to evaluate the sharpness of an image from the pixel distribution [23, 24].…”

Section: Introductionmentioning

confidence: 99%

Fast autofocusing of recorded planes by salient feature region for coherent diffraction imaging

Zhou,

Li,

Qiao

et al. 2023

Journal of Biophotonics

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In multi‐distance coherent diffraction imaging (MDCDI), the task of distance calculation for multi‐diffraction images is cumbersome. The information features are hard‐to‐extract and the region of interest extraction algorithms are difficult to be adopted. A universal salient feature region selection algorithm by using the area with the highest density of corners is proposed to extract the most representative feature region. In addition, equally spaced recording modes and mismatched diffraction distances will result in system noise and destroy image quality. The polydirectional maximum gradient is offered as a sharpness criterion to weigh a quantitative feature for the final pattern. A fast, sensitive, and high‐accuracy autofocusing and sample reconstruction can be achieved using only a small number of images while ensuring that morphological properties and quantification of the reconstructions are not compromised. The proposed method is promising for biological and medical dynamic observations for computational imaging systems.This article is protected by copyright. All rights reserved.

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“…In addition, such applications cannot fully satisfy patients' autonomy requirements when resting at home. Addressing these shortcomings is crucial for developing remote healthcare schemes [17,18]. Regarding the use of digital applications in remote healthcare, various novel topics have received increasing attention, such as improving communication functions and components [19,20], long-term health care applications [21], integration of cloud databases [22], and the development of artificial intelligence technology [23].…”

Section: Introductionmentioning

confidence: 99%

A Fast and Effective System for Detection of Neonatal Jaundice with a Dynamic Threshold White Balance Algorithm

Hsu

Cheng

2021

Healthcare

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Neonatal jaundice is caused by high levels of bilirubin in the body, which most commonly appears within three days of birth among newborns. Neonatal jaundice detection systems can take pictures in different places and upload them to the system for judgment. However, the white balance problem of the images is often encountered in these detection systems. The color shift images induced by different light haloes will result in the system causing errors in judging the images. The true color of images is very important information when the detection system judges the jaundice value. At present, most systems adopt specific assumption methods and rely on color charts to adjust images. In this study, we propose a novel white balance method with dynamic threshold to screen appropriate feature factors at different color temperatures iteratively and make the adjustment results of different images close to the same. The experimental results indicate that the proposed method achieves superior results in comparison with several traditional approaches.

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Automatic Compensation for Defects of Laser Reflective Patterns in Optics-Based Auto-Focusing Microscopes

Cited by 17 publications

References 27 publications

Innovative Image Processing Method to Improve Autofocusing Accuracy

Innovative Image Processing Method to Improve Autofocusing Accuracy

Fast autofocusing of recorded planes by salient feature region for coherent diffraction imaging

A Fast and Effective System for Detection of Neonatal Jaundice with a Dynamic Threshold White Balance Algorithm

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