Industrial Image Enhancement Method Based on Cloud Edge Fusion

Zhang, Shuai; Pei, Caiyan; Sun, Dejie; Liu, Wenyuan; Cao, Lu

doi:10.1155/2022/7717161

Cited by 2 publications

(4 citation statements)

References 28 publications

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“…(1) Information entropy (IE) IE [40] measures the degree of system orderliness. A lower IE indicates a more organized system, while a higher IE denotes a more chaotic system.…”

Section: Weld X-ray Image Enhancement Experimentsmentioning

confidence: 99%

“…The PSNR [40], which represents the proportion of maximal probable signal power to the noise power affecting accuracy for its representation, is often used to measure the adequacy of an image processing procedure. As shown in Table 2, the IE of the image exceeded that of the Laplace enhancement algorithm after image enhancement.…”

Section: Weld X-ray Image Enhancement Experimentsmentioning

confidence: 99%

“…Therefore, weld defects were more prominent (focus on the region), while some of the weld seam detail information was less affected by noise.The two enhancement algorithms were compared and analyzed using three parameters commonly used to assess the Laplace algorithm to objectively evaluate the quality of the results after negative weld image processing. (1) Information entropy (IE) IE[40] measures the degree of system orderliness. A lower IE indicates a more organized system, while a higher IE denotes a more chaotic system.…”

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confidence: 99%

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Preprocessing method for digital X-Ray weld images

Xie,

Liu,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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Pipelines represent the main mode of transportation for oil and gas, and failure caused by weld defects is the primary cause of accidents, presenting significant risks to personal and environmental safety. Therefore, regularly inspecting pipeline welds is essential for reducing accidents, ensuring personal safety, protecting the environment, and achieving sustainable development. Although manual photographic X-ray inspection is widely utilized for the detection of weld defects in various industries, this process is challenging since X-ray images are noisy and unclear, with uneven grey values. This study proposed a noise reduction framework by introducing a Wiener filter into the wavelet domain to reduce noise in X-ray images while minimizing information loss. Furthermore, a comprehensive evaluation factor that combined contrast and the noise reduction level was proposed to reduce the dependence of image processing performance on wavelet thresholds. Additionally, this study improved the Laplace method by adaptively adjusting the normal and tangential diffusion coefficients to enhance the weld X-ray image contrast without increasing the noise. Through qualitative comparison and quantitative analysis, it has been determined that the suggested methods exhibit better properties than alternative industrial pipeline weld X-ray image processing algorithms. This superiority is observed in objective values as well as subjective visualizations.

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“…(1) Information entropy (IE) IE [40] measures the degree of system orderliness. A lower IE indicates a more organized system, while a higher IE denotes a more chaotic system.…”

Section: Weld X-ray Image Enhancement Experimentsmentioning

confidence: 99%

Section: Weld X-ray Image Enhancement Experimentsmentioning

confidence: 99%

mentioning

confidence: 99%

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Preprocessing method for digital X-Ray weld images

Xie,

Liu,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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“…Dengan kata lain image enhancement bersifat subjektif, dan hal-hal yang biasa dilakukan di langkah ini adalah pengaturan seperti brightness, contrast, edge enhancement, pseudo coloring, sharpening dan magnifying [1], [2]. Ide dasar ari image enhancement adalah memperbesar contrast antara area terang dan gelap [3]. Image restoration adalah langkah ketiga dimana langkah ini berbeda dari image enhancement yang bersifat subjektif, image restoration bersifat objektif [1].…”

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Perbandingan Algoritma untuk Mereduksi Noise pada Citra Digital

Nugroho

Hazmin

2022

Journal-ITA

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Image restoration is one of the stages in the field of Digital Image Processing. Image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabillistic models of image degradation. The mathematical algorithm to reduce noise in digital images in this study uses 8 filtering algorithm methods. The purpose of this study is to compare 8 filtering algorithm and conclude which algorithm is the best for reducing noise in digital images. The method for generating noise uses Rayleigh Noise and Erlang (Gamma) Noise. The algorithm for reducing noise is Arithmetic Mean Filter, Geometric Mean Filter, Harmonic Mean Filter, Contraharmonic Mean Filter, Geometric Mean Filter, Harmonic Mean Filter, Contraharmonic Mean Filter, Median Filter, Maximum Filter, Minimum Filter, and Midpoint Filter. The measurement to determine which algorithm is the best using Root Mean Square Error (RMSE). Tests were carried out on 15 digital images by testing 1200 times. The conclusion of this study is that the best algorithm for noise reduction is Median Filter by resulting the smallest RMSE value of 6.0860942.

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Industrial Image Enhancement Method Based on Cloud Edge Fusion

Cited by 2 publications

References 28 publications

Preprocessing method for digital X-Ray weld images

Preprocessing method for digital X-Ray weld images

Perbandingan Algoritma untuk Mereduksi Noise pada Citra Digital

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