Improved local histogram equalization with gradient-based weighting process for edge preservation

Lai, Yu-Ren; Tsai, Ping-Chuan; Yao, Chih‐Yuan; Ruan, Shanq-Jang

doi:10.1007/s11042-015-3147-7

Cited by 25 publications

(18 citation statements)

References 30 publications

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“…Histogram equalization is a fully automatic and fast contrast improvement technique. In the recent literature can be found numerous methods of contrast enhancing such as based on the bi-histogram equalization median plateau limit [19], exposure based sub-image histogram equalization [20], edge preserving local histogram equalization [21] and many others. In this paper authors used classic histogram equalization with global information.…”

Section: Proposed Methodsmentioning

confidence: 99%

Grain size determination and classification using adaptive image segmentation with shape information for milling quality evaluation

Budzan

Pawełczyk

2018

Diagnostyka

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In this paper, authors described methods of material granularity evaluation and a novel method for grain size determination with inline electromagnetic mill device diagnostics. The milling process quality evaluation can be carried out with vibration measurements, analysis of the milling material images or well-known screening machines. The method proposed in this paper is developed to the online examination of the milled product during the milling process using real-time digital images. In this paper, authors concentrated their work on copper ore milling process. Determination of the total number of the grain, the size of each grain, also the classification of the grains are the main goal of the developed method. In the proposed method the vision camera with lightning mounted at two assumed angles has been used. The detection of the grains has been based on an adaptive segmentation algorithm, improved with distance transform to enhance grains detection. The information about particles shape and context is used to optimize the grain classification process in the next step. The final classification is based on the rule-based method with defined particle shape and size parameters.

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Section: Proposed Methodsmentioning

confidence: 99%

Grain size determination and classification using adaptive image segmentation with shape information for milling quality evaluation

Budzan

Pawełczyk

2018

Diagnostyka

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“…In order to improve the gray resolution of the image, gray correction can be made to the image. In this study, the image was corrected by the histogram equalization method [8].…”

Section: Gray Level Correctionmentioning

confidence: 99%

Building crack monitoring based on digital image processing

Cai

et al. 2020

Frattura Ed Integrità Strutturale

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Building crack monitoring is of great value to the judgment of building safety. In this study, the digital image processing technology was studied and applied to the monitoring of building cracks. Crack images were collected by CCD camera, and then operations such as graying, correction, denoising and segmentation were carried out to obtain clear crack images. The obtained images were processed morphologically to further improve the quality. Finally, the width and length of cracks were calculated. In the case analysis, the results of 15 cracks measured by a microscope were taken as the standards and compared with the calculated results. The results showed that the results calculated in this study and the manual measurement results differed little, and the average errors of the width and length were 0.021 mm and 0.024 mm respectively, which suggested that the method proposed had a high reliability. The findings of this study provide a new idea for the further development of the building crack monitoring field and is conducive to the accurate assessment of building safety.

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“…Histogram equalization (HE) is considered one of the most popular techniques to improve contrast because of its simplicity and ease of implementation. It generates a uniform output histogram by means of stretching the dynamic range of the input image histogram, thereby improving the image contrast [ 45 , 46 ]. However, HE often produces undesirable artifacts because of over enhancement when a few consecutive gray levels occupy substantial areas in an image and considerably changes the mean brightness of the input image [ 45 , 46 , 47 , 48 , 49 ].…”

Section: Introductionmentioning

confidence: 99%

“…It generates a uniform output histogram by means of stretching the dynamic range of the input image histogram, thereby improving the image contrast [ 45 , 46 ]. However, HE often produces undesirable artifacts because of over enhancement when a few consecutive gray levels occupy substantial areas in an image and considerably changes the mean brightness of the input image [ 45 , 46 , 47 , 48 , 49 ]. In addition, HE may also cause loss of details since some gray levels with a smaller proportion pixel number are combined to form a certain gray level [ 49 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, HE may also cause loss of details since some gray levels with a smaller proportion pixel number are combined to form a certain gray level [ 49 ]. To avoid shortcomings of the classic HE, many techniques and improved algorithms on the basis of histogram equalization have been proposed in the past and have been widely utilized in the field of image enhancement [ 41 , 42 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ]. Since preservation of the original brightness is crucial to avoid undesirable artifacts, the brightness preserving bi-histogram equalization (BBHE) [ 50 ] is developed to preserve the original brightness to a certain extent by individually equalizing two sub-histograms based on the mean value of the images.…”

Section: Introductionmentioning

confidence: 99%

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A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images

Zhang

Celenk

et al. 2018

Sensors

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Enabled by piezoceramic transducers, ultrasonic logging images often suffer from low contrast and indistinct local details, which makes it difficult to analyze and interpret geologic features in the images. In this work, we propose a novel partially overlapped sub-block histogram-equalization (POSHE)-based optimum clip-limit contrast enhancement (POSHEOC) method to highlight the local details hidden in ultrasonic well logging images obtained through piezoceramic transducers. The proposed algorithm introduces the idea of contrast-limited enhancement to modify the cumulative distribution functions of the POSHE and build a new quality evaluation index considering the effects of the mean gradient and mean structural similarity. The new index is designed to obtain the optimal clip-limit value for histogram equalization of the sub-block. It makes the choice of the optimal clip-limit automatically according to the input image. Experimental results based on visual perceptual evaluation and quantitative measures demonstrate that the proposed method yields better quality in terms of enhancing the contrast, emphasizing the local details while preserving the brightness and restricting the excessive enhancement compared with the other seven histogram equalization-based techniques from the literature. This study provides a feasible and effective method to enhance ultrasonic logging images obtained through piezoceramic transducers and is significant for the interpretation of actual ultrasonic logging data.

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Improved local histogram equalization with gradient-based weighting process for edge preservation

Cited by 25 publications

References 30 publications

Grain size determination and classification using adaptive image segmentation with shape information for milling quality evaluation

Grain size determination and classification using adaptive image segmentation with shape information for milling quality evaluation

Building crack monitoring based on digital image processing

A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images

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