Recursive weighted multi-plateau histogram equalization for image enhancement

Qadar, Muhammad Ali; Yan, Zhaowen; Rehman, Ali; Alvi, Muhammad Adnan

doi:10.1016/j.ijleo.2015.08.278

Cited by 21 publications

(7 citation statements)

References 18 publications

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“…Dimana dalam penelitian tersebut dilakukan penilaian performa kualitas citra dengan menggunakan ekstraksi fitur entropy dan deviasi standar. Pada penelitian M. Ali juga melakukan penilaian kualitas hasil histogram menggunakan deviasi standar [8] [9].…”

Section: Pendahuluanunclassified

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Image Enhancement Pada Screen Capture CCTV Dengan Menggunakan Metode Histogram EKualisasi

Nugroho

2017

KINETIK

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Penggunaan kamera Closed Circuit Television (CCTV) banyak digunakan saat ini terutama perusahaan atau industry, pertokoan dan tempat-temapat strategis lainnya. Dengan menggunakan kamera CCTV keamanan tempat tersebut akan terjamin. Akan tetapi kekurangan dari kamera CCTV adalah apabila ruangan tidak memiliki cahaya kurang (gelap) maka hasil objek yang terekam tidak maksimal. Untuk mengatasi permasalahan diatas maka perlu adanya Histogram Ekualisasi yang dapat memberikan perbaikan kualitas citra (Image Enhancement). Untuk citra yang diambil sebagai Image Enhancement sebagai contoh uji coba penelitian adalah citra Screen Capture CCTV. Dari citra Screen Capture CCTV tersebut dilakukan tahap-tahap pemrosesan citra untuk menghasilkan perbaikan kualitas citra yang baik. Rencana pengambilan data citra screen Capture CCTV sejumlah 5 citra. Cara kerja sistem informasi Image Enhancement adalah citra screen Capture CCTV di praproses dengan ukuran 200x260 piksel BMP, kemudia citra di GarayScale untuk dijadikan nilai piksel yang seragam, selajatunya citra di histogram ekualisasi untuk intensitas menjadi seragam. Untuk menguji tingkat kualitas citra menggunakan metode ekstraksi tekstur histogram berbasis rata-rata intensitas dan deviasi standar.

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Section: Pendahuluanunclassified

“…Untuk mendapatkan tekstur berbasis Histogram diambil 2 fitur yang dipilih, yaitu rata-rata intensitas dan deviasi standar (DS) [8][11]. Fitur pertama yang dihitung secara statistis adalah average intensitas.…”

Section: Tingkat Kualitas Citraunclassified

Image Enhancement Pada Screen Capture CCTV Dengan Menggunakan Metode Histogram EKualisasi

Nugroho

2017

KINETIK

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“…By contrast, regions comprising a relatively small number of pixels may be eliminated, resulting in the so-called washed-out appearance. 24 In order to solve this issue, the improved histogram equalization technique studies [25][26][27][28][29][30][31][32][33][34][35][36] divide the image into several sub-regions for histogram equalization. The original brightness of the image is maintained, therefore avoiding excessive enhancement of image information.…”

Section: Introductionmentioning

confidence: 99%

“…This method created visually pleasant enhancement effects while eliminating the problem of over enhancement. Qadar et al proposed the recursive weighted multi‐plateau histogram equalization (RWMPHE) to segment the histogram into two or more sub histograms followed by clipping with six plateau limits. The weighting module modified the probability density of each sub‐histogram by the normalized power law before histogram equalization.…”

Section: Introductionmentioning

confidence: 99%

Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Kuo

2019

Int J Imaging Syst Tech

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In order to enhance the pathological features of medical images and aid the medical diagnosis, the image enhancement is a necessary process. This study presented the Gaussian probability model combining with bi‐histogram equalization to enhance the contrast of pathological features in medical images. There are five different bi‐histogram equalizations, namely, bi‐histogram equalization (BBHE), dualistic sub‐image histogram equalization (DSIHE), bi‐histogram equalization with a plateau limit (BHEPL), bi‐histogram equalization median plateau limit (BHEPL‐D), and bi‐histogram equalization with modified histogram bins (BHEMHB). The entropy, contrast, absolute mean brightness error (AMBE), and skewness difference are used to quantize the enhancement results. From the experimental result, it is observed that the entropy and contrast of the images can be effectively enhanced by using Gaussian probability bi‐histogram equalizations, and the Gaussian probability bi‐histogram equalization median plateau limit (GPBHEPL‐D) has the best enhanced result. The proposed GPBHEPL‐D method is effective in strengthening the pathological features in medical images, so as to increase the efficiency of doctors' diagnoses and computer‐aided detection.

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“…Finally, all subimages are merged into a single image on the global grayscale histogram. The improved algorithms include brightness-preserving bi-HE (BBHE) [13,14], recursive mean-separate HE (RMSHE) [15][16][17], dualistic subimage HE (DSIHE) [18,19], minimum mean brightness error bi-HE (MMBEBHE) [20,21], and weighting meanseparated sub-HE (WMSHE) [22,23].…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancement Method Based on Gray and Its Distance Double-Weighting Histogram Equalization for 3D CT Images of PCBs

Zeng

Yan

Wang³

2016

Mathematical Problems in Engineering

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Cone beam computed tomography (CBCT) is a new detection method for 3D nondestructive testing of printed circuit boards (PCBs). However, the obtained 3D image of PCBs exhibits low contrast because of several factors, such as the occurrence of metal artifacts and beam hardening, during the process of CBCT imaging. Histogram equalization (HE) algorithms cannot effectively extend the gray difference between a substrate and a metal in 3D CT images of PCBs, and the reinforcing effects are insignificant. To address this shortcoming, this study proposes an image enhancement algorithm based on gray and its distance double-weighting HE. Considering the characteristics of 3D CT images of PCBs, the proposed algorithm uses gray and its distance double-weighting strategy to change the form of the original image histogram distribution, suppresses the grayscale of a nonmetallic substrate, and expands the grayscale of wires and other metals. The proposed algorithm also enhances the gray difference between a substrate and a metal and highlights metallic materials. The proposed algorithm can enhance the gray value of wires and other metals in 3D CT images of PCBs. It applies enhancement strategies of changing gray and its distance double-weighting mechanism to adapt to this particular purpose. The flexibility and advantages of the proposed algorithm are confirmed by analyses and experimental results.

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Recursive weighted multi-plateau histogram equalization for image enhancement

Cited by 21 publications

References 18 publications

Image Enhancement Pada Screen Capture CCTV Dengan Menggunakan Metode Histogram EKualisasi

Image Enhancement Pada Screen Capture CCTV Dengan Menggunakan Metode Histogram EKualisasi

Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Contrast Enhancement Method Based on Gray and Its Distance Double-Weighting Histogram Equalization for 3D CT Images of PCBs

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