Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis

Zeng, Zhiqiang; Zhang, Rui; Cai, Wenan; Li, Yanfeng

doi:10.1109/access.2022.3173326

Cited by 2 publications

(2 citation statements)

References 28 publications

(28 reference statements)

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“…This results in a more evenly distributed histogram, and an image with improved contrast. If Histogram Equalization is performed on an image, the pixel intensities of the image are distributed over a broader range in the histogram graph [25]. In this way, the quality of the image is improved by enhancing the contrast using Histogram Equalization.…”

Section: Histogram Equalizationmentioning

confidence: 99%

Enhanced Pneumonia Diagnosis Using Chest X-Ray Image Features and Multilayer Perceptron and k-NN Machine Learning Algorithms

Çelik¹,

Demi̇rel²

2023

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Pneumonia poses a significant risk of mortality, particularly in individuals with compromised immune systems, necessitating early diagnosis and treatment to combat the disease effectively. In this study, we employed Multilayer Perceptron (MLP) and k-Nearest Neighbors (k-NN) machine learning (ML) algorithms to facilitate pneumonia diagnosis using preprocessed Chest X-ray images. Preprocessing steps, including Histogram Equalization, Mask R-CNN (Mask Region-Based Convolutional Neural Network), and Otsu thresholding, were successively performed on the images. Textural features were subsequently extracted from the Chest X-ray images and utilized as inputs for the classification algorithms. To address the imbalanced class problem in the training data, the Synthetic Minority Over-sampling Technique (SMOTE) was implemented. Classification evaluation metrics included accuracy, precision, recall, F1-Score, and AUC Score (Area Under Curve Score). The results revealed that the MLP algorithm outperformed the k-NN algorithm across all metrics. Furthermore, a comparison of the MLP and k-NN algorithms with previous studies in the literature demonstrated the superiority of the MLP algorithm, achieving an accuracy of 95.673%, F1-Score of 95.706%, and AUC Score of 99.006%. This study highlights the potential of employing the MLP algorithm for highly accurate pneumonia diagnosis using Chest X-ray images.

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Section: Histogram Equalizationmentioning

confidence: 99%

Enhanced Pneumonia Diagnosis Using Chest X-Ray Image Features and Multilayer Perceptron and k-NN Machine Learning Algorithms

Çelik¹,

Demi̇rel²

2023

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show abstract

“…Hence, depending on the frequency of pixels in the image, some scholars proposed modified histogram equalization based on histogram clipping [28], histogram frequency weighting etc. Furthermore, other novel strategies, such as local histogram cropping [29], linear regression algorithm [30], the limited wavelet integer coefficient [31], partial statistic and global mapping model [32], are also introduced to further optimize the histogram equalization. LHE divides an image into blocks by means of overlapping [17,33], non-overlapping [18,34,35], or partially overlapping [8], and then fusing the enhanced subblocks to reconstruct high-quality images.…”

Section: Introductionmentioning

confidence: 99%

Retinex‐inspired contrast stretch and detail boosting for lowlight image enhancement

Liu

Lan

et al. 2023

IET Image Processing

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Lowlight images with low brightness and contrast, blurry details usually bring us an uncomfortable visual experience. To promote the quality of these deviation images, this paper presents a new and efficient approach, named MFMR, for enhancing lowlight images in the hue‐saturation‐value (HSV) colour space. Concretely, the multi‐angle filter is first applied to estimate the artifact‐free illumination and reflection component of the V‐channel. Afterward, the adaptive bi‐interval histogram with human visual characteristics and morphological operations is employed to process the former, adaptive gamma correction to process the latter for generating various feature maps. In the end, these feature maps are united via adaptive multi‐scale fusion strategy to reconstruct high‐quality images, which are characterized by high contrast and brightness, vivid colour, and clearer details. Extensive experiments show that this method is a well‐proven low‐light image enhancement approach, which outperforms the state‐of‐the‐art comparison methods. Furthermore, the proposed method also can yield satisfying images in the heavy foggy, yellow sand, underwater, and other severe conditions.

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Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis

Cited by 2 publications

References 28 publications

Enhanced Pneumonia Diagnosis Using Chest X-Ray Image Features and Multilayer Perceptron and k-NN Machine Learning Algorithms

Enhanced Pneumonia Diagnosis Using Chest X-Ray Image Features and Multilayer Perceptron and k-NN Machine Learning Algorithms

Retinex‐inspired contrast stretch and detail boosting for lowlight image enhancement

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