Adaptive Smoothness Constraint Image Multilevel Fuzzy Enhancement Algorithm

Chu, Xi; Zhou, Zhixiang; Yang, Chaoshan; Xiang, Xiaoju

doi:10.17576/jsm-2019-4812-19

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…There are some distinctions between fuzzy and conventional conceptual conceptions of sets. A degree of membership will be used to indicate the degree to which different types of fuzzy sets belong [27]. First, it will assume a collection of data 𝑋 = {𝑥 1 , 𝑥 2 .…”

Section: Methods and Materials 21 Fuzzy Entropymentioning

confidence: 99%

Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet

Khazal

Alhaleem

2022

IJEECS

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<span>Many types of medical pictures have to be fused, as single-modality medical images can give limited information because of the imagery and the complicated architecture of the human organ. This study proposes to offer a platform on which to make clinical diagnoses and to increase the accuracy of the target identification and the quality of the fused pictures by combining the benefits of nonsubsampled contourlet transform (NSCT) and fuzzy entropy. A picture is first broken down into low frequency or high frequency subbands through NSCT. In line with the various features of the low and high frequency components the respective fusion rules must be implemented. It calculates the level of membership of low frequency coefficients. The fusion of coefficients is also calculated and then utilized to retain picture features. By increasing regional energy, high-frequency components are merged. Inverse transformation produces the final fused picture. Experimental results have shown that, based on subjective visual effect and objective assessment standards, the suggested technique produces a satisfactory fusion effect. This process may also achieve high average gradient, standard deviation (SD), and edge preservation and maintain the fused picture features well. Effective reference can be provided by the outcome of the suggested algorithm for patients' assessment.</span>

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Section: Methods and Materials 21 Fuzzy Entropymentioning

confidence: 99%

Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet

Khazal

Alhaleem

2022

IJEECS

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“…Consequently, edge detection operators fail to detect probability edges in the most effective ways. For this reason, the existing edge detection algorithm-that is, an obscure edge pixel point group-is replaced with an optimal edge line, and can be modified and optimized in subsequent research to reduce errors and improve measurement accuracy and algorithm stability [46,47]. Concerned with environmental interference and noise influence, noise reduction anti-disturbance autoencoders (i.e., denoising AE (DAE) and contractive AE (CAE)) are introduced into this paper with the goal of lowering relevant errors and improving measurement accuracy and algorithm stability.…”

Section: Holographic Visual Sensor Based Characterization Parameters mentioning

confidence: 99%

Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor

Shao

Zhou

Deng

et al. 2020

Sensors

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To further improve the precision and efficiency of structural health monitoring technology and the theory of large-scale structures, full-field non-contact structural geometry morphology monitoring is expected to be a breakthrough technology in structural safety state monitoring and digital twins, owing to its economic, credible, high frequency, and holographic advantages. This study validates a proposed holographic visual sensor and algorithms in a computer-vision-based full-field non-contact displacement and vibration measurement. Using an automatic camera patrol experimental device, original segmental dynamic and static video monitoring data of a model bridge under various damage/activities were collected. According to the temporal and spatial characteristics of the series data, the holographic geometric morphology tracking algorithm was introduced. Additionally, the feature points set of the structural holography geometry and the holography feature contours were established. Experimental results show that the holographic visual sensor and the proposed algorithms can extract an accurate holographic full-field displacement signal, and factually and sensitively accomplish vibration measurement, while accurately reflecting the real change in structural properties under various damage/action conditions. The proposed method can serve as a foundation for further research on digital twins for large-scale structures, structural condition assessment, and intelligent damage identification.

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Adaptive Smoothness Constraint Image Multilevel Fuzzy Enhancement Algorithm

Cited by 2 publications

References 19 publications

Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet

Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet

Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor

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