Fine edge detection in single-pixel imaging

Zhou, Liyu; Huang, Xianwei; Fu, Qin; Zou, Xuanpengfan; Bai, Yanfeng; Fu, Xiquan

doi:10.3788/col202119.121101

Cited by 19 publications

(7 citation statements)

References 23 publications

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“…When h(x, y) satisfies the equation h(x, y) = h(−x, −y), the order of the two integrals in Eq. ( 5) can be exchanged, [35] and Eq. ( 5) can be rewritten as…”

Section: The Model and Theorymentioning

confidence: 99%

Ghost imaging based on the control of light source bandwidth

et al. 2023

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A scheme to improve the quality in ghost imaging (GI) by controlling the bandwidth of light source (BCGI) is proposed. The theoretical and numerical results show that the reconstruction result with high quality can be obtained by adjusting the bandwidth range of the light source appropriately, and the selection criteria of the bandwidth is analyzed by the power distribution of imaging target. A proof-of-principle experiment is implemented to verify the theoretical and numerical results. In addition, BCGI also presents better anti-noise performance when compared with some popular GI methods.

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“…When h(x, y) satisfies the equation h(x, y) = h(−x, −y), the order of the two integrals in Eq. ( 5) can be exchanged, [35] and Eq. ( 5) can be rewritten as…”

Section: The Model and Theorymentioning

confidence: 99%

Ghost imaging based on the control of light source bandwidth

et al. 2023

Self Cite

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“…DIP-based non-contact measurements involve edge-detection algorithms, which focus on extracting edge pixels with large grayscale variations in the target image. The edge detection operator identifies the pixel-level edges of an image using first-or second-order derivative operators, and thus the edges are judged by the grayscale variations within the neighborhood of the image pixels [10][11][12]. The existing popular edge detection operators, such as the Roberts operator [13], Laplacian operator [14], Prewitt operator [15], Sobel operator [16], and Canny operator [17], can achieve edge detection of images at the pixel level.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation

Tang

Chen

et al. 2023

Sensors

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Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.

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“…Since the use of the traditional operators cannot have accurate profile extraction, researchers have come up with various novel ways for the image enhancement. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] Among them, mathematical method has been intensively used for image enhancement. One good example is shown in the application of the Hausdorff derivative, which is used for image edge extraction.…”

Section: Introductionmentioning

confidence: 99%

“…[12] The other research efforts for acquiring the image enhancement involved with the method of using wavelets. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] The wavelets are generally wave-like oscillatory functions. They show a recovery impact since the features acquired via the wavelets show different resolutions.…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

et al. 2023

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Image enhancement is important given that it can be used to highlight the area of interest in the images. This article designs four filters via special function for realizing image enhancement. Firstly, a filter based on the exponential function is designed. When the value of the progression is even, the edge feature can be extracted. When the value of the progression is odd, sharp contrast can be obtained. Secondly, a filter is built using hyperbolic cosine and its inverse function, where a printmaking feature can be extracted. Thirdly, a filter is made via a hyperbolic secant function and its inverse. It can lead to the extraction of image edge. When the progression value is increasing, marginal effect can be found and the brightness is decreasing. Ripple morphology can be found. Fourthly, a filter is constructed through a hyperbolic sine function and its inverse, where marginal features can be extracted. Furthermore, these filters are useful for extracting the marginal features even when a high noise density of 0.9 is added to the original images. They are useful for highlighting the images acquired from near infrared imaging.

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Fine edge detection in single-pixel imaging

Cited by 19 publications

References 23 publications

Ghost imaging based on the control of light source bandwidth

Ghost imaging based on the control of light source bandwidth

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

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