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Journal articles on the topic 'Adaptive histogram equalization'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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1

Stark, J. A., and W. J. Fitzgerald. "Model-based adaptive histogram equalization." Signal Processing 39, no. 1-2 (September 1994): 193–200. http://dx.doi.org/10.1016/0165-1684(94)90133-3.

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2

Peng, Na Xin, and Yu Qiang Chen. "Improved Self-Adaptive Image Histogram Equalization Algorithm." Advanced Materials Research 760-762 (September 2013): 1495–500. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1495.

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Histogram equalization (HE) algorithm is wildly used method in image processing of contrast adjustment using images histogram. This method is useful in images with backgrounds and foreground that are both bright or both dark. But the performance of HE is not satisfactory to images with backgrounds and foregrounds that are both bright or both dark. To deal with the above problem, [ gives an improved histogram equalization algorithm named self-adaptive image histogram equalization (SIHE) algorithm. Its main idea is to extend the gray level of the image which firstly be processed by the classical histogram equalization algorithm. This paper gives detailed introduction to SIHE and analyzes the shortage of it, then give an improved version of SIHE named ISIHE, finally do experiments to show the performance of our algorithm.
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Jbara, Wurood A., and Rafah A. Jaafar. "MRI Medical Images Enhancement based on Histogram Equalization and Adaptive Histogram Equalization." International Journal of Computer Trends and Technology 50, no. 2 (August 25, 2017): 91–93. http://dx.doi.org/10.14445/22312803/ijctt-v50p116.

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4

Wijaya Kusuma, I. Wayan Angga, and Afriliana Kusumadewi. "PENERAPAN METODE CONTRAST STRETCHING, HISTOGRAM EQUALIZATION DAN ADAPTIVE HISTOGRAM EQUALIZATION UNTUK MENINGKATKAN KUALITAS CITRA MEDIS MRI." Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 11, no. 1 (April 30, 2020): 1–10. http://dx.doi.org/10.24176/simet.v11i1.3153.

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Citra medis adalah suatu pola atau gambar dua dimensi bagian dalam tubuh manusia yang digunakan oleh ahli kesehatan untuk mendeteksi dan menganalisa penyakit pasien. Pada bidang radiologi citra yang sering digunakan saat ini adalah citra Magnetic resonance Imaging (MRI). Keunggulan citra MRI adalah kemampuan menampilkan detail anatomi secara jelas dalam berbagai potongan (multiplanar) tanpa mengubah posisi pasien. Citra MRI ini akan digunakan oleh dokter ataupun peneliti untuk melakukan analisis ada tidaknya suatu tumor, kanker, atau kelainan pada pasien. Penelitian ini mengusulkan metode Contrast Stretching, Histogram Equalization dan Adaptive Histogram Equalization untuk meningkatkan kualitas citra medis. Batasan masalah penelitian ini adalah citra medis MRI yang digunakan sebagai obyek penelitian adalah citra medis MRI Otak baik yang normal atau yang mengalami lesi (gangguan). Dari hasil kualitas citra dan analisa kuantitatif menunjukkan bahwa metode contrast stretching menghasilkan hasil kualitas citra MRI jauh lebih baik dibandingkan dengan metosde histogram equalization, dan adaptive histogram equalization. Nilai MSE yang paling rendah adalah pada metode contrast stretching yaitu 0,00346. Sedangkan nilai MSE yang paling besar dihasilkan oleh metode histogram equalization. Kualitas citra dengan metode contrast stretching menghasilkan nilai PSNR yang paling besar yaitu 22,0677. Ini menandakan bahwa kualitas citra dari metode contrast stretching jauh lebih baik dibandingkan metode histogram equalization, dan adaptive histogram equalization.
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Pizer, Stephen M., E. Philip Amburn, John D. Austin, Robert Cromartie, Ari Geselowitz, Trey Greer, Bart ter Haar Romeny, John B. Zimmerman, and Karel Zuiderveld. "Adaptive histogram equalization and its variations." Computer Vision, Graphics, and Image Processing 39, no. 3 (September 1987): 355–68. http://dx.doi.org/10.1016/s0734-189x(87)80186-x.

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Stimper, Vincent, Stefan Bauer, Ralph Ernstorfer, Bernhard Scholkopf, and Rui Patrick Xian. "Multidimensional Contrast Limited Adaptive Histogram Equalization." IEEE Access 7 (2019): 165437–47. http://dx.doi.org/10.1109/access.2019.2952899.

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Mustaghfirin, Fathan, Erwin, Hadrians Kesuma Putra, Umi Yanti, and Rahma Ricadonna. "The Comparison of Iris Detection Using Histogram Equalization and Adaptive Histogram Equalization Methods." Journal of Physics: Conference Series 1196 (March 2019): 012016. http://dx.doi.org/10.1088/1742-6596/1196/1/012016.

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8

Abood, Loay Kadom. "Contrast enhancement of infrared images using Adaptive Histogram Equalization (AHE) with Contrast Limited Adaptive Histogram Equalization (CLAHE)." Iraqi Journal of Physics (IJP) 16, no. 37 (September 11, 2018): 127–35. http://dx.doi.org/10.30723/ijp.v16i37.84.

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The objective of this paper is to improve the general quality of infrared images by proposes an algorithm relying upon strategy for infrared images (IR) enhancement. This algorithm was based on two methods: adaptive histogram equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The contribution of this paper is on how well contrast enhancement improvement procedures proposed for infrared images, and to propose a strategy that may be most appropriate for consolidation into commercial infrared imaging applications.The database for this paper consists of night vision infrared images were taken by Zenmuse camera (FLIR Systems, Inc) attached on MATRIC100 drone in Karbala city. The experimental tests showed significant improvements.
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Zhao, Yu Qian, and Zhi Gang Li. "FPGA Implementation of Real-Time Adaptive Bidirectional Equalization for Histogram." Advanced Materials Research 461 (February 2012): 215–19. http://dx.doi.org/10.4028/www.scientific.net/amr.461.215.

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According to the characteristics of infrared images, a contrast enhancement algorithm was presented. The principium of FPGA-based adaptive bidirectional plateau histogram equalization was given in this paper. The plateau value was obtained by finding local maximum and whole maximum in statistical histogram based on dimensional histogram statistic. Statistical histogram was modified by the plateau value and balanced in gray scale and gray spacing. Test data generated by single frame image, which was simulated by FPGA-based real-time adaptive bidirectional plateau histogram equalization. The simulation results indicates that the precept meet the requests well in both the image processing effects and processing speed
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10

Lawton, Sahil, and Serestina Viriri. "Detection of COVID-19 from CT Lung Scans Using Transfer Learning." Computational Intelligence and Neuroscience 2021 (April 8, 2021): 1–14. http://dx.doi.org/10.1155/2021/5527923.

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This paper aims to investigate the use of transfer learning architectures in the detection of COVID-19 from CT lung scans. The study evaluates the performances of various transfer learning architectures, as well as the effects of the standard Histogram Equalization and Contrast Limited Adaptive Histogram Equalization. The findings of this study suggest that transfer learning-based frameworks are an alternative to the contemporary methods used to detect the presence of the virus in patients. The highest performing model, the VGG-19 implemented with the Contrast Limited Adaptive Histogram Equalization, on a SARS-CoV-2 dataset, achieved an accuracy and recall of 95.75% and 97.13%, respectively.
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Chen, Wei, Kohei Inoue, and Kenji Hara. "Adaptive Aggregated Histogram Equalization for Color Image Enhancement without Gamut Problem." Journal of the Institute of Industrial Applications Engineers 8, no. 2 (April 25, 2020): 56–62. http://dx.doi.org/10.12792/jiiae.8.56.

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Paranjape, Raman B., William M. Morrow, and Rangaraj M. Rangayyan. "Adaptive-neighborhood histogram equalization for image enhancement." CVGIP: Graphical Models and Image Processing 54, no. 3 (May 1992): 259–67. http://dx.doi.org/10.1016/1049-9652(92)90056-4.

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Buzuloiu, Vasile. "Adaptive-neighborhood histogram equalization of color images." Journal of Electronic Imaging 10, no. 2 (April 1, 2001): 445. http://dx.doi.org/10.1117/1.1353200.

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14

Sherrier, Robert H., and G. A. Johnson. "Regionally Adaptive Histogram Equalization of the Chest." IEEE Transactions on Medical Imaging 6, no. 1 (March 1987): 1–7. http://dx.doi.org/10.1109/tmi.1987.4307791.

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Xianfang Sun, P. L. Rosin, R. R. Martin, and F. C. Langbein. "Bas-Relief Generation Using Adaptive Histogram Equalization." IEEE Transactions on Visualization and Computer Graphics 15, no. 4 (July 2009): 642–53. http://dx.doi.org/10.1109/tvcg.2009.21.

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16

Stark, J. A., and W. J. Fitzgerald. "An Alternative Algorithm for Adaptive Histogram Equalization." Graphical Models and Image Processing 58, no. 2 (March 1996): 180–85. http://dx.doi.org/10.1006/gmip.1996.0015.

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17

Santhi, K., and R. S. D. Wahida Banu. "Adaptive contrast enhancement using modified histogram equalization." Optik - International Journal for Light and Electron Optics 126, no. 19 (October 2015): 1809–14. http://dx.doi.org/10.1016/j.ijleo.2015.05.023.

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18

Nurhidayah, Bannu Abdul Samad, and Bualkar Abdullah. "Perbandingan Metode Contrast Enhancement pada Citra CT-Scan Kanker Paru-paru." Gravitasi 19, no. 2 (December 31, 2020): 24–28. http://dx.doi.org/10.22487/gravitasi.v19i2.15360.

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Abstrak: Di Indonesia kanker paru menjadi penyebab kematian kedua setelah kanker payudara. Angka mortalitas yang cukup tinggi, maka penentuan diagnosis lebih awal memegang peranan yang sangat penting dalam manajemen terapi. Kelemahan CT-Scan dalam mendiagnosa kanker paru-paru disebabkan oleh kontras citra yang rendah dan derau pada citra. Pada penelitian ini akan membandingkan metode contrast enhancement berbasis histogram equalization dan contrast limited adaptive histogram equalization untuk meningkatkan kualitas citra dengan menggunakan software Matlab. Namun, sebelumnya dilakukan reduksi noise dengan menggunakan metode median filter. Kinerja dari setiap metode dihitung dengan mencari nilai MSE (Mean Square Error) dan PSNR (Peak Signal to Noise Ratio) citra. Dari nilai MSE dan PSNR yang di dapatkan diperoleh nilai MSE dan PSNR terbaik pada metode contrast limited adaptive histogram equalization dengan nilai 653,434 dB dan 245,547 dB.
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Gopal, Shefali Srivastava, and Smriti Srivastava. "Biometric authentication using local subspace adaptive histogram equalization." Journal of Intelligent & Fuzzy Systems 32, no. 4 (March 29, 2017): 2893–99. http://dx.doi.org/10.3233/jifs-169232.

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Riadi, Aditya Akbar, Ahmad Abdul Chamid, and Akh Sokhibi. "ANALISIS KOMPARASI METODE PERBAIKAN KONTRAS BERBASIS HISTOGRAM EQUALIZATION PADA CITRA MEDIS." Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 8, no. 1 (April 1, 2017): 383–88. http://dx.doi.org/10.24176/simet.v8i1.1026.

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Citra merupakan gambaran tentang karakteristik suatu obyek menurut kondisi variabel tertentu. Pengolahan citra bertujuan memperbaiki kualitas citra agar mudah diinterpretasi oleh manusia atau mesin (dalam hal ini komputer). Terdapat beberapa operasi di dalam pengolahan citra, salah satunya adalah perbaikan kontras yang pada dasarnya biasa digunakan untuk memunculkan bagian-bagian yang tidak terlihat (hidden feature) pada citra. Hasil citra dari rontgen yang tidak selalu memiliki kualitas citra yang baik, seperti halnya hasil citra x-ray yang terlalu gelap atau ada bagian tulang yang terlihat samar sehingga gambar tidak terlihat jelas. Pada penelitian ini teknik peningkatan citra dengan perbaikan kontras menggunakan metode berbasis Histrogram Equalization. Pada citra medis tersebut dan juga menunjukkan kinerja hasil pengukuran kontrol eror menggunakan Mean Square Error menjelaskan bahwa metode Contrast Limited Adaptive Histogram Equalization lebih baik dibandingkan dengan metode Histrogram Equalization dan metode Adaptive Histogram Equalization.
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Zhuang, Liyun, and Yepeng Guan. "Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization." Computational Intelligence and Neuroscience 2018 (August 13, 2018): 1–13. http://dx.doi.org/10.1155/2018/3837275.

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A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.
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Sari, Ni Larasati Kartika, Maria Oktavianti, and Samsun Samsun. "Analisis Karakter Segmen Abnormal pada Citra Mamografi dengan Menggunakan Berbagai Metode Preprocessing Citra." Jurnal Ilmiah Giga 22, no. 1 (January 15, 2020): 1. http://dx.doi.org/10.47313/jig.v22i1.737.

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Penelitian ini menganalisis pengaruh penerapan beberapa jenis algoritma preprocessing untuk mencari karakteristik segmen abnormal yang tampak pada citra mamografi. Mamografi merupakan pemeriksaan radiografi khusus payudara. Penerapan algoritma preprocessing yang terdiri dari metode filtering, contrast enhancement, sharpening, dan smoothing diharapkan dapat mengurangi noise dan meningkatkan kontras citra mamografi serta membantu ahli radiologi untuk melakukan diagnosis pada citra. Pada penelitian ini akan digunakan dua algoritma filtering yaitu median filter dan gaussian filter. Selain itu digunakan dua algoritma contrast enhancement yaitu global histogram equalization dan CLAHE (Contrast Limited Adaptive Histogram Equalization). Nilai piksel rata-rata segmen abnormal berkisar antara 206.9-213.3 dan rasio sumbu minor/mayor segmen abnormal berkisar antara 0.5-0.7.Pemilihan jenis metode filter (median filter dan gaussian filter) tidak mempengaruhi hasil nilai piksel rata-rata maupun rasio sumbu minor/mayor dan ukuran segmen abnormal, namun pemilihan jenis metode peningkatan kontras (CLAHE dan global histogram equalization) menghasilkan segmen abnormal dengan ukuran yang berbeda. Metode global histogram equalization menghasilkan segmen abnormal yang tidak dapat dibedakan dengan sekitarnya sehingga hasil ekstrasi segmen terlalu besar.
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Stark, J. A. "Adaptive image contrast enhancement using generalizations of histogram equalization." IEEE Transactions on Image Processing 9, no. 5 (May 2000): 889–96. http://dx.doi.org/10.1109/83.841534.

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24

Kim, Taekyung, and Joonki Paik. "Adaptive contrast enhancement using gain-controllable clipped histogram equalization." IEEE Transactions on Consumer Electronics 54, no. 4 (November 2008): 1803–10. http://dx.doi.org/10.1109/tce.2008.4711238.

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Rehm, Kelly, George W. Seeley, William J. Dallas, Theron W. Ovitt, and Joachim F. Seeger. "Design and testing of artifact-suppressed adaptive histogram equalization." Journal of Thoracic Imaging 5, no. 1 (January 1990): 85–91. http://dx.doi.org/10.1097/00005382-199001000-00013.

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26

Anand, S., and S. Gayathri. "Mammogram image enhancement by two-stage adaptive histogram equalization." Optik 126, no. 21 (November 2015): 3150–52. http://dx.doi.org/10.1016/j.ijleo.2015.07.069.

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27

Upadhyay, Jayprakash, and Ayushi Jaiswal. "A joint implementation of adaptive histogram equalization and interpolation." Optik 126, no. 24 (December 2015): 5936–40. http://dx.doi.org/10.1016/j.ijleo.2015.08.150.

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28

Liu, Chengwei, Xiubao Sui, Xiaodong Kuang, Yuan Liu, Guohua Gu, and Qian Chen. "Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram." Remote Sensing 11, no. 11 (June 10, 2019): 1381. http://dx.doi.org/10.3390/rs11111381.

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In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.
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Román, Julio César Mello, Vicente R. Fretes, Carlos G. Adorno, Ricardo Gariba Silva, José Luis Vázquez Noguera, Horacio Legal-Ayala, Jorge Daniel Mello-Román, Ricardo Daniel Escobar Torres, and Jacques Facon. "Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology." Sensors 21, no. 9 (April 29, 2021): 3110. http://dx.doi.org/10.3390/s21093110.

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Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.
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Liu, Dong Mei, Tao Zhang, Chuan Li Yin, and Xiao Qiang Ji. "An Embedded Infrared Image Enhancement System Based on DSP and FPGA." Advanced Materials Research 505 (April 2012): 263–66. http://dx.doi.org/10.4028/www.scientific.net/amr.505.263.

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According to the disadvantage of the large noises of histogram equalization algorithm, a new adaptive image enhancement algorithm is presented. First, the statistical histogram of the infrared image is done. Then the threshold of plateaus Equalization is calculated and the statistical histogram is modified. Finally the bright values of the pixels of the image are changed. An embedded high speed image enhancement processing system on high performance DSP TMS320DM642 and FPGA was designed. Experimental results with real images shown that the system can improve the contrast of the infrared image, limit the noises of the enhancement image, and effectively enhance the infrared image, the running time of the program is shorter, so it can meet the requirements of real-time in the project.
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Singh, Jagdeep, and Vijay Kumar Banga. "An Enhanced DCT based Image Fusion using Adaptive Histogram Equalization." International Journal of Computer Applications 87, no. 12 (February 14, 2014): 26–32. http://dx.doi.org/10.5120/15262-3955.

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Sim, Kok Swee, Desmond Teck Kiang Kho, Mohsen Esmaeilinia, Yang Lee, and Chung Sheng Ee. "Graphic User Interface for Extreme Level Eliminating Adaptive Histogram Equalization." Journal of Image and Graphics 4, no. 1 (2016): 42–45. http://dx.doi.org/10.18178/joig.4.1.42-45.

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Wibawa, Kadek Suar, Gusti Made Arya Sasmita, and Kadek Suar Wibawa. "Adaptive Histogram Equalization to Increase the Percentage of Face Recognition." International Journal of Computer Applications Technology and Research 8, no. 12 (December 1, 2019): 446–51. http://dx.doi.org/10.7753/ijcatr0812.1002.

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Kaur, Savroop, and Hartej S. Dadhwal. "Biorthogonal Wavelet Transform Using Bilateral Filter and Adaptive Histogram Equalization." International Journal of Intelligent Systems and Applications 7, no. 3 (February 8, 2015): 37–43. http://dx.doi.org/10.5815/ijisa.2015.03.05.

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XU, Li-Min. "Research on Robust Speaker Identification Based on Adaptive Histogram Equalization." Acta Automatica Sinica 34, no. 7 (March 2, 2009): 752–59. http://dx.doi.org/10.3724/sp.j.1004.2008.00752.

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Chang, Yakun, Cheolkon Jung, Peng Ke, Hyoseob Song, and Jungmee Hwang. "Automatic Contrast-Limited Adaptive Histogram Equalization With Dual Gamma Correction." IEEE Access 6 (2018): 11782–92. http://dx.doi.org/10.1109/access.2018.2797872.

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37

Yang Ni, F. Devos, M. Boujrad, and Jian Hong Guan. "Histogram-equalization-based adaptive image sensor for real-time vision." IEEE Journal of Solid-State Circuits 32, no. 7 (July 1997): 1027–36. http://dx.doi.org/10.1109/4.597293.

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XU Hong-lie, 许轰烈, 匡. 程. KUANG Cheng, 张. 乐. ZHANG Le, 李. 莎. LI Sha, 王树军 WANG Shu-jun, 汤. 峥. TANG Zheng, and 李琳娜 LI Lin-na. "Range limited adaptive brightness preserving multi-threshold histogram equalization algorithm." Chinese Optics 10, no. 6 (2017): 726–36. http://dx.doi.org/10.3788/co.20171006.0726.

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39

Braunstein, E. M., P. Capek, K. Buckwalter, P. Bland, and C. R. Meyer. "Adaptive histogram equalization in digital radiography of destructive skeletal lesions." Radiology 166, no. 3 (March 1988): 883–85. http://dx.doi.org/10.1148/radiology.166.3.3340789.

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40

Safrit, H. D., J. R. Perry, E. V. Staab, S. M. Pizer, R. E. Johnston, A. Geaelowitz, and R. C. Cromartie. "28 CLINICAL ASSESSMENT OF INTENSITY WINDOWING AND ADAPTIVE HISTOGRAM EQUALIZATION." Investigative Radiology 21, no. 9 (September 1986): S8. http://dx.doi.org/10.1097/00004424-198609000-00046.

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Zimmerman, John B., Steve B. Cousins, Karin M. Hartzell, Mark E. Frisse, and Michael G. Kahn. "A psychophysical comparison of two methods for adaptive histogram equalization." Journal of Digital Imaging 2, no. 2 (May 1989): 82–91. http://dx.doi.org/10.1007/bf03168024.

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42

Harichandana, M., V. Sowmya, V. V. Sajithvariyar, and R. Sivanpillai. "COMPARISON OF IMAGE ENHANCEMENT TECHNIQUES FOR RAPID PROCESSING OF POST FLOOD IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-2-2020 (November 17, 2020): 45–50. http://dx.doi.org/10.5194/isprs-archives-xliv-m-2-2020-45-2020.

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Abstract. Satellite images are widely used for assessing the areal extent of flooded areas. However, presence of clouds and shadow limit the utility of these images. Numerous digital algorithms are available for enhancing such images and highlighting areas of interest. These algorithms range from simple to complex, and the time required to process these images also varies considerably. For disaster response, it is important to select an algorithm that can enhance the quality of the images in relatively short time. This study compared the relative performance of five traditional (Histogram Equalization, Local Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Gamma Correction, and Linear Contrast Stretch) algorithms for enhancing post-flood satellite images. Flood images with different levels of clouds and shadows were enhanced and output generated were evaluated in terms of processing time and quality as measured by Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), a no-reference image quality metric. Findings from this study will provide valuable information to image analysts for selecting a suitable algorithm for rapidly processing post-flood satellite images.
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Kenyta, Claudia, and Daniel Martomanggolo Wonohadidjojo. "Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android." Ultimatics : Jurnal Teknik Informatika 12, no. 2 (December 29, 2020): 80–88. http://dx.doi.org/10.31937/ti.v12i2.1667.

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When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.
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V, Jyothisree, and Smitha Dharan. "Shadow Detection Using Tricolor Attenuation Model Enhanced with Adaptive Histogram Equalization." International Journal of Computer Science and Information Technology 5, no. 2 (April 30, 2013): 147–55. http://dx.doi.org/10.5121/ijcsit.2013.5213.

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Zhu, Youlian, and Cheng Huang. "An Adaptive Histogram Equalization Algorithm on the Image Gray Level Mapping." Physics Procedia 25 (2012): 601–8. http://dx.doi.org/10.1016/j.phpro.2012.03.132.

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Magudeeswaran, V., and J. Fenshia Singh. "Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images." International Journal of Imaging Systems and Technology 27, no. 1 (March 2017): 98–103. http://dx.doi.org/10.1002/ima.22214.

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Subramani, Bharath, and Magudeeswaran Veluchamy. "MRI brain image enhancement using brightness preserving adaptive fuzzy histogram equalization." International Journal of Imaging Systems and Technology 28, no. 3 (March 25, 2018): 217–22. http://dx.doi.org/10.1002/ima.22272.

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Majeed, Samer Hameed, and Nor Ashidi Mat Isa. "Iterated Adaptive Entropy-Clip Limit Histogram Equalization for Poor Contrast Images." IEEE Access 8 (2020): 144218–45. http://dx.doi.org/10.1109/access.2020.3014453.

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Fayad, Laura M., Yinpeng Jin, Andrew F. Laine, Yahya M. Berkmen, Gregory D. Pearson, Benjamin Freedman, and Ronald Van Heertum. "Chest CT Window Settings with Multiscale Adaptive Histogram Equalization: Pilot Study." Radiology 223, no. 3 (June 2002): 845–52. http://dx.doi.org/10.1148/radiol.2233010943.

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Tae Keun Kim, Joon Ki Paik, and Bong Soon Kang. "Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering." IEEE Transactions on Consumer Electronics 44, no. 1 (1998): 82–87. http://dx.doi.org/10.1109/30.663733.

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