Relevant bibliographies by topics / Adaptive histogram equalization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Adaptive histogram equalization'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

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Journal articles on the topic "Adaptive histogram equalization"

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

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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 (2017): 91–93. http://dx.doi.org/10.14445/22312803/ijctt-v50p116.

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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 (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, et al. "Adaptive histogram equalization and its variations." Computer Vision, Graphics, and Image Processing 39, no. 3 (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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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 (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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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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Dissertations / Theses on the topic "Adaptive histogram equalization"

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Kurak, Charles W. Jr. "Adaptive Histogram Equalization, a Parallel Implementation." UNF Digital Commons, 1990. http://digitalcommons.unf.edu/etd/260.

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Adaptive Histogram Equalization (AHE) has been recognized as a valid method of contrast enhancement. The main advantage of AHE is that it can provide better contrast in local areas than that achievable utilizing traditional histogram equalization methods. Whereas traditional methods consider the entire image, AHE utilizes a local contextual region. However, AHE is computationally expensive, and therefore time-consuming. In this work two areas of computer science, image processing and parallel processing, are combined to produce an efficient algorithm. In particular, the AHE algorithm is implemented with a Multiple-Instruction-Multiple-Data (MIMD) parallel architecture. It is proposed that, as MIMD machines become more powerful and prevalent, this methodology can be applied to not only this particular algorithm, but also to many others in its class.
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Kvapil, Jiří. "Adaptivní ekvalizace histogramu digitálních obrazů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228687.

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The diploma thesis is focused on histogram equalization method and his extension by the adaptive boundary. This thesis contains explanations of basic notions on that histogram equalization method was created. Next part is described the human vision and priciples of his imitation. In practical part of this thesis was created software that makes it possible to use methods of adaptive histogram equalization on real images. At the end is showed some results that was reached.
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Yakoubian, Jeffrey Scott. "Adaptive histogram equalization for mammographic image processing." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/16387.

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Mallampati, Vivek. "Image Enhancement & Automatic Detection of Exudates in Diabetic Retinopathy." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18109.

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Diabetic retinopathy (DR) is becoming a global health concern, which causes the loss of vision of most patients with the disease. Due to the vast prevalence of the disease, the automated detection of the DR is needed for quick diagnoses where the progress of the disease is monitored by detection of exudates changes and their classifications in the fundus retina images. Today in the automated system of the disease diagnoses, several image enhancement methods are used on original Fundus images. The primary goal of this thesis is to make a comparison of three of popular enhancement methods of the Mahalanobis Distance (MD), the Histogram Equalization (HE) and the Contrast Limited Adaptive Histogram Equalization (CLAHE). By quantifying the comparison in the aspect of the ability to detect and classify exudates, the best of the three enhancement methods is implemented to detect and classify soft and hard exudates. A graphical user interface is also adopted, with the help of MATLAB. The results showed that the MD enhancement method yielded better results in enhancement of the digital images compared to the HE and the CLAHE. The technique also enabled this study to successfully classify exudates into hard and soft exudates classification. Generally, the research concluded that the method that was suggested yielded the best results regarding the detection of the exudates; its classification and management can be suggested to the doctors and the ophthalmologists.
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Naram, Hari Prasad. "Classification of Dense Masses in Mammograms." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1528.

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This dissertation material provided in this work details the techniques that are developed to aid in the Classification of tumors, non-tumors, and dense masses in a Mammogram, certain characteristics such as texture in a mammographic image are used to identify the regions of interest as a part of classification. Pattern recognizing techniques such as nearest mean classifier and Support vector machine classifier are also used to classify the features. The initial stages include the processing of mammographic image to extract the relevant features that would be necessary for classification and during the final stage the features are classified using the pattern recognizing techniques mentioned above. The goal of this research work is to provide the Medical Experts and Researchers an effective method which would aid them in identifying the tumors, non-tumors, and dense masses in a mammogram. At first the breast region extraction is carried using the entire mammogram. The extraction is carried out by creating the masks and using those masks to extract the region of interest pertaining to the tumor. A chain code is employed to extract the various regions, the extracted regions could potentially be classified as tumors, non-tumors, and dense regions. Adaptive histogram equalization technique is employed to enhance the contrast of an image. After applying the adaptive histogram equalization for several times which will provide a saturated image which would contain only bright spots of the mammographic image which appear like dense regions of the mammogram. These dense masses could be potential tumors which would need treatment. Relevant Characteristics such as texture in the mammographic image are used for feature extraction by using the nearest mean and support vector machine classifier. A total of thirteen Haralick features are used to classify the three classes. Support vector machine classifier is used to classify two class problems and radial basis function (RBF) kernel is used to find the best possible (c and gamma) values. Results obtained in this research suggest the best classification accuracy was achieved by using the support vector machines for both Tumor vs Non-Tumor and Tumor vs Dense masses. The maximum accuracies achieved for the tumor and non-tumor is above 90 % and for the dense masses is 70.8% using 11 features for support vector machines. Support vector machines performed better than the nearest mean majority classifier in the classification of the classes. Various case studies were performed using two distinct datasets in which each dataset consisting of 24 patients’ data in two individual views. Each patient data will consist of both the cranio caudal view and medio lateral oblique views. From these views the region of interest which could possibly be a tumor, non-tumor, or a dense regions(mass).
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Martišek, Karel. "Adaptive Filters for 2-D and 3-D Digital Images Processing." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2012. http://www.nusl.cz/ntk/nusl-234150.

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Práce se zabývá adaptivními filtry pro vizualizaci obrazů s vysokým rozlišením. V teoretické části je popsán princip činnosti konfokálního mikroskopu a matematicky korektně zaveden pojem digitální obraz. Pro zpracování obrazů je volen jak frekvenční přístup (s využitím 2-D a 3-D diskrétní Fourierovy transformace a frekvenčních filtrů), tak přístup pomocí digitální geometrie (s využitím adaptivní ekvalizace histogramu s adaptivním okolím). Dále jsou popsány potřebné úpravy pro práci s neideálními obrazy obsahujícími aditivní a impulzní šum. Závěr práce se věnuje prostorové rekonstrukci objektů na základě jejich optických řezů. Veškeré postupy a algoritmy jsou i prakticky zpracovány v softwaru, který byl vyvinut v rámci této práce.
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Martišek, Karel. "Adaptivní filtry pro 2-D a 3-D zpracování digitálních obrazů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2012. http://www.nusl.cz/ntk/nusl-234015.

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Práce se zabývá adaptivními filtry pro vizualizaci obrazů s vysokým rozlišením. V teoretické části je popsán princip činnosti konfokálního mikroskopu a matematicky korektně zaveden pojem digitální obraz. Pro zpracování obrazů je volen jak frekvenční přístup (s využitím 2-D a 3-D diskrétní Fourierovy transformace a frekvenčních filtrů), tak přístup pomocí digitální geometrie (s využitím adaptivní ekvalizace histogramu s adaptivním okolím). Dále jsou popsány potřebné úpravy pro práci s neideálními obrazy obsahujícími aditivní a impulzní šum. Závěr práce se věnuje prostorové rekonstrukci objektů na základě jejich optických řezů. Veškeré postupy a algoritmy jsou i prakticky zpracovány v softwaru, který byl vyvinut v rámci této práce.
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Hsieh, Wen-lung, and 謝文龍. "Study of global contrast enhancement by adaptive histogram equalization." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/64296881409979898418.

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碩士<br>雲林科技大學<br>電機工程系碩士班<br>98<br>HDR image of the formation of two approaches, one relying on pieces of the same image with different exposure and then re-capture the visual details of the composition of a single image; Second, contrast is used to expand a single image and then compressed into a high dynamic contrast of the image . Available in only a single high dynamic range images, how to make low-contrast display can honestly show their beautiful natural scenes? In general there are two methods for using a simple contrast change quickly get results, but may lose the bright part or shadow detail; Second, we use the dark part of the Department of Imaging bright layer technology to improve the use of Gaussian filters, the details Although can present, but its slow, heavy and generally paint a sense of visual experience fit. This paper we propose a scalable and compressed the image contrast of the method, in RGB color model, using the control image divided by the coefficient between the global image brightness can change the purpose, in the supplemented by adaptive histogram equalization technique to improve LDR & HDR image. LDR image can be divided into the more dark images and general images, we selected the general image contrast amplification factor, and then another set of coefficients selected so that dark images become invisible acceptable visual images without having to delete. HDR image can be divided into three categories, we were also selected most of the coefficients and set rules so that the face for processing images, can be easily visualized. The proposed methodology is straightforward, and in the experiment, compared with some traditional methods to improve the outcome after, could easily have found that the proposed method can generally get a fine image and HDR image detail, contrast, and consistent visual performance feeling images.
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CHEN, ZHI-FAN, and 陳志凡. "An Image Enhancement Method Based on Bilateral Filtering and Contrast Limited Adaptive Histogram Equalization." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/77m8t9.

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碩士<br>國立中正大學<br>資訊管理系研究所<br>104<br>At present, digital photography technology can’t be precisely presented as the scene seen by the human eye since the display device is typically low dynamic range rather than high dynamic range. In other words, the devices are often unable to display the details of shadows and highlights at the same time for high contrast images. If a normal image enhancement method is used to enhance these images, it may result in uneven distribution of image brightness, color distortion or loss of image detail information. Therefore, this study proposes a method to resolve these problems. Starting with the use of the bilateral filter to retain image details, then automatically give the optimum operation parameters through contrast limited adaptive histogram equalization to make appropriate contrast adjustment to the base layer image, so the display of the device can be more similar to the visual quality of the high dynamic range. In the experiments, in comparison with other state-of-art methods, we find that the proposed method is superior to other methods whether in detail information, retention of hue or brightness enhancement. In addition, there is better performance in the objective mathematical evaluation indexes.
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Li, Wei-Jia, and 李尉嘉. "Enhancing Low-exposure Images Based on Modified Histogram Equalization and Local Contrast Adaptive Enhancement." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/03340322721691697800.

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碩士<br>國立中興大學<br>資訊科學與工程學系<br>104<br>Image enhancement methods can effectively improve the visual contents of images, provide us with the better visual experience, and make the computer work more efficiently on images. Therefore, enhanced images tend to be more suitable than original images from the perspective of a particular application. Two common drawbacks usually exist in traditional image enhancement methods: one is over-enhancement and the other is loss of details. In this thesis, we propose an adaptive method to enhance the illumination of color images. The method consists of two steps for performing image enhancement. The first step is to use adjust the content of the image based on image histogram to decrease non-natural points and avoid the situation of over-brightness. The second step applies adaptive local contrast enhancement algorithm to reduce the loss of details. Experimental results show that the brightness and contrast of low-exposure images can be effectively improved by our method. As compared with other methods, our method has better performance in terms of objective measurements such as Contrast, Entropy, Gradient andAbsolute Mean Brightness Error (AMBE).
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Book chapters on the topic "Adaptive histogram equalization"

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Austin, John D., and Stephen M. Pizer. "A Multiprocessor Adaptive Histogram Equalization Machine." In Information Processing in Medical Imaging. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-7263-3_25.

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Kunnath, Neeth Xavier, and Suk-Ho Lee. "Meanshift Segmentation Guided Spatially Adaptive Histogram Equalization." In Computer Science and its Applications. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45402-2_100.

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Sahoo, Subhasmita, Jagyanseni Panda, and Mihir Narayan Mohanty. "Adaptive Bi-Histogram Equalization Using Threshold (ABHET)." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3433-6_19.

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Halder, Amiya, Apurba Sarkar, and Sneha Ghose. "Adaptive Histogram Equalization and Opening Operation-Based Blood Vessel Extraction." In Soft Computing in Data Analytics. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0514-6_54.

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Mohan, Shelda, and M. Ravishankar. "Modified Contrast Limited Adaptive Histogram Equalization Based on Local Contrast Enhancement for Mammogram Images." In Mobile Communication and Power Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35864-7_60.

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Garima Yadav, Saurabh Maheshwari, and Anjali Agarwal. "Multi-domain Image Enhancement of Foggy Images Using Contrast Limited Adaptive Histogram Equalization Method." In Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2638-3_4.

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Goyal, Vishal, and Aasheesh Shukla. "An Enhancement of Underwater Images Based on Contrast Restricted Adaptive Histogram Equalization for Image Enhancement." In Smart Innovations in Communication and Computational Sciences. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5345-5_25.

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de Graaf, Cornelis N., Christianus J. G. Bakker, Jan J. Koenderink, and Peter P. van Rijk. "Some Aspects of Mr Image Processing and Display: Simulation Studies, Multiresolution Segmentation, and Adaptive Histogram Equalization." In Information Processing in Medical Imaging. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4261-5_4.

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Muneeswaran, V., and M. Pallikonda Rajasekaran. "Local Contrast Regularized Contrast Limited Adaptive Histogram Equalization Using Tree Seed Algorithm—An Aid for Mammogram Images Enhancement." In Smart Intelligent Computing and Applications. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1921-1_67.

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Kim, Hyoung-Joon, Jong-Myung Lee, Jin-Aeon Lee, Sang-Geun Oh, and Whoi-Yul Kim. "Contrast Enhancement Using Adaptively Modified Histogram Equalization." In Advances in Image and Video Technology. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949534_116.

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Conference papers on the topic "Adaptive histogram equalization"

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Pizer, Stephen M., John D. Austin, Robert Cromartie, et al. "Algorithms For Adaptive Histogram Equalization." In Physics and Engineering of Computerized Multidimensional Imaging and Processing, edited by Thomas F. Budinger, Zang-Hee Cho, and Orhan Nalcioglu. SPIE, 1986. http://dx.doi.org/10.1117/12.966688.

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Gillespy III, Thurman. "Optimized algorithm for adaptive histogram equalization." In Medical Imaging '98, edited by Kenneth M. Hanson. SPIE, 1998. http://dx.doi.org/10.1117/12.310830.

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Wang, Zhiming, and Jianhua Tao. "A Fast Implementation of Adaptive Histogram Equalization." In 2006 8th international Conference on Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/icosp.2006.345602.

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Shen, Hongying, Shuifa Sun, Bangjun Lei, and Sheng Zheng. "An adaptive brightness preserving bi-histogram equalization." In Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), edited by Jianguo Liu, Mingyue Ding, and Zhong Chen. SPIE, 2011. http://dx.doi.org/10.1117/12.902215.

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Ogawa, Koichi, Atsuhisa Saito, Masato Nakajima, Yutaka Ando, and Shozo Hashimoto. "Regional adaptive histogram equalization using fuzzy sets." In Medical Imaging '90, Newport Beach, 4-9 Feb 90, edited by Murray H. Loew. SPIE, 1990. http://dx.doi.org/10.1117/12.18914.

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Cosman, Pamela C., Eve A. Riskin, and Robert M. Gray. "Combined vector quantization and adaptive histogram equalization." In Medical Imaging VI, edited by Yongmin Kim. SPIE, 1992. http://dx.doi.org/10.1117/12.59501.

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Jin, Yinpeng, Laura M. Fayad, and Andrew F. Laine. "Contrast enhancement by multiscale adaptive histogram equalization." In International Symposium on Optical Science and Technology, edited by Andrew F. Laine, Michael A. Unser, and Akram Aldroubi. SPIE, 2001. http://dx.doi.org/10.1117/12.449705.

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Noor, Noorhayati Mohamed, Noor Elaiza Abdul Khalid, Mohd Hanafi Ali, and Alice Demi Anak Numpang. "Fish Bone Impaction Using Adaptive Histogram Equalization (AHE)." In 2010 Second International Conference on Computer Research and Development. IEEE, 2010. http://dx.doi.org/10.1109/iccrd.2010.84.

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Amorim, Paulo, Thiago Moraes, Jorge Silva, and Helio Pedrini. "3D Adaptive Histogram Equalization Method for Medical Volumes." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006615303630370.

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Zhang, Zhigao, Hongmei Zhang, and Zhili Pei. "Adaptive Equalization Algorithm for Image Based on Histogram." In 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering. Atlantis Press, 2014. http://dx.doi.org/10.2991/meic-14.2014.292.

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