• Title/Summary/Keyword: histogram equalization

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Application of Local Histogram and Plateau Equalization Algorithm for Contrast Enhancement of Real Time Thermal Image (실시간 열영상 대조비 개선을 위한 대역추출 및 플래토 평활화 알고리즘 적용)

  • 조흥기;김수곤;전희종
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.53 no.2
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    • pp.76-85
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    • 2004
  • In this paper, the contrast enhancement method of thermal image is proposed and it is the plateau equalization algorithm using local histogram for the real time display of infrared imagery. Through hardware implementing, its practicality and adequacy are proved. Examinations are executed to verify the effect of contrast enhancement by bright control and contrast control automatic to the plateau value in the manual mode, and that verified the effect of contrast enhancement in the automatic mode and the practicality in the real system. According to the experiment results, the proposed "the application of local histogram and plateau equalization algorithm for contrast enhancement of real time thermal image"in this dissertation is the verified method for the thermal imaging contrast enhancement.

Robust Speech Recognition by Utilizing Class Histogram Equalization (클래스 히스토그램 등화 기법에 의한 강인한 음성 인식)

  • Suh, Yung-Joo;Kim, Hor-Rin;Lee, Yun-Keun
    • MALSORI
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    • no.60
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    • pp.145-164
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    • 2006
  • This paper proposes class histogram equalization (CHEQ) to compensate noisy acoustic features for robust speech recognition. CHEQ aims to compensate for the acoustic mismatch between training and test speech recognition environments as well as to reduce the limitations of the conventional histogram equalization (HEQ). In contrast to HEQ, CHEQ adopts multiple class-specific distribution functions for training and test environments and equalizes the features by using their class-specific training and test distributions. According to the class-information extraction methods, CHEQ is further classified into two forms such as hard-CHEQ based on vector quantization and soft-CHEQ using the Gaussian mixture model. Experiments on the Aurora 2 database confirmed the effectiveness of CHEQ by producing a relative word error reduction of 61.17% over the baseline met-cepstral features and that of 19.62% over the conventional HEQ.

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Histogram Equalization Algorithm for Suppressing Over-Enhancement and Enhancing Edges (과대 대조 강조 방지 및 엣지 강화를 동시에 수행하는 히스토그램 평활화 알고리듬)

  • Mun, Junwon;Kim, Jaeseok
    • Journal of Korea Multimedia Society
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    • v.22 no.9
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    • pp.983-991
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    • 2019
  • Histogram equalization method is a popular contrast enhancement technique. However, there are some drawbacks, namely, over-enhancement, under-enhancement, structure information loss, and noise amplification. In this paper, we propose an edge-enhancing histogram equalization algorithm while suppressing over-enhancement simultaneously. Firstly, over-enhancement is suppressed by clipping a transfer function, then, edge enhancement is achieved by using guided image filter. Experiments are carried out to evaluate the performance of the various HE algorithms. As a result, both qualitative and quantitative assessment showed that the proposed algorithm successfully suppressed over-enhancement while enhancing edges.

A Image Contrast Enhancement by Clustering of Image Histogram (영상의 히스토그램 군집화에 의한 영상 대비 향상)

  • Hong, Seok-Keun;Lee, Ki-Hwan;Cho, Seok-Je
    • Journal of the Institute of Convergence Signal Processing
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    • v.10 no.4
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    • pp.239-244
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    • 2009
  • Image contrast enhancement has an important role in image processing applications. Conventional contrast enhancement techniques, histogram stretching and histogram equalization, and many methods based on histogram equalization often fail to produce satisfactory results for broad variety of low-contrast images. So, this paper proposes a new image contrast enhancement method based on the clustering method. The number of cluster of histogram is found by analysing the histogram of original image. The histogram components is classified using K-means algorithm. And then these histogram components are performed histogram stretching and histogram equalization selectively by comparing cluster range with pixel rate of cluster. From the expremental results, the proposed method was more effective than conventional contrast enhancement techniques.

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Evaluation of U-Net Based Learning Models according to Equalization Algorithm in Thyroid Ultrasound Imaging (갑상선 초음파 영상의 평활화 알고리즘에 따른 U-Net 기반 학습 모델 평가)

  • Moo-Jin Jeong;Joo-Young Oh;Hoon-Hee Park;Joo-Young Lee
    • Journal of radiological science and technology
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    • v.47 no.1
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    • pp.29-37
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    • 2024
  • This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.

Color Image Enhancement Using Local Area Histogram Equalization On Segmented Regions Via Watershed Transform

  • Lertpokanont, B.;Chitwong, S.;Cheevasuvit, F.;Dejhan, K.
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.192-194
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    • 2003
  • Since the details in quasi-homogeneous region will be destroyed from the conventional global image enhancement method such as histogram equalization. This defect is caused by the saturation of gray level in equalization process. So the local histogram equalization for each quasi-homogeneous region will be used in order to improve the details in the region itself. To obtain the quasi- homogeneous regions, the original image must be segmented. Here we applied the watershed transform to the interesting image. Since the watershed transform is based on mathematical morphology, therefore, the regions touch can be effectively separated. Hence two adjacent regions which have the similar gray pixels will be split off. The process will be independently applied to three different spectral images. Then three different colors are assigned to each processed image in order to produce a color composite image. By the proposed algorithm, the result image shows the better perception on image details. Therefore, the high efficiency of image classification can be obtained by using this color image.

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Contrast Image Enhancement Using Multi-Histogram Equalization

  • Phanthuna, Nattapong;cheevasuwit, Fusak
    • International Journal of Advanced Culture Technology
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    • v.3 no.2
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    • pp.161-170
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    • 2015
  • Mean separated histogram equalization in order to preserve the original mean brightness has been proposed. To provide the minimum mean brightness error after the histogram modification, the input image's histogram is successively divided by the factor of 2 until the mean brightness error is satisfied the defined threshold. Then each divided group or sub-histogram will be independently equalized based on the proportional input mean. To provide the overall minimum mean brightness error, each group will be controlled by adding some certain pixels from the adjacent grey level of the next group for giving its mean near by the corresponding the divided mean. However, it still exists some little error which will be put into the next adjacent group. By successive dividing the original histogram, we found that the absolute mean brightness error is gradually decreased when the number of group is increased. Therefore, the error threshold is assigned in order to automatically dividing the original histogram for obtaining the desired absolute mean brightness error (AMBE). This process will be applied to the color image by treating each color independently.

Image Recognition Based on Nonlinear Equalization and Multidimensional Intensity Variation (비선형 평활화와 다차원의 명암변화에 기반을 둔 영상인식)

  • Cho, Yong-Hyun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.5
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    • pp.504-511
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    • 2014
  • This paper presents a hybrid recognition method, which is based on the nonlinear histogram equalization and the multidimensional intensity variation of an images. The nonlinear histogram equalization based on a adaptively modified function is applied to improve the quality by adjusting the brightness of the image. The multidimensional intensity variation by considering the a extent of 4-step changes in brightness between the adjacent pixels is also applied to reflect accurately the attributes of image. The statistical correlation that is measured by the normalized cross-correlation(NCC) coefficient, is applied to comprehensively measure the similarity between the images. The NCC is considered by the intensity variation of each 2-direction(x-axis and y-axis) image. The proposed method has been applied to the problem for recognizing the 50-face images of 40*40 pixels. The experimental results show that the proposed method has a superior recognition performances to the method without performing the histogram equalization, or the linear histogram equalization, respectively.

Image Quality Enhancement by Using Logistic Equalization Function (로지스틱 평활화 함수에 의한 영상의 화질개선)

  • Cho, Yong-Hyun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.1
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    • pp.30-35
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    • 2010
  • This paper presents a quality enhancement of images by using a histogram equalization based on the symmetric logistic function. The histogram equalization is a simple and effective spatial processing method that it enhances the quality by adjusting the brightness of image. The logistic function that is a sigmoidal nonlinear transformation function, is applied to non-linearly enhance the brightness of the image according to its intensity level frequency. We propose a flexible and symmetrical logistic function by only using the intensity with maximum frequency in an histogram and the total number of pixels. The proposed function decreases the computation load of an exponential function in the traditional logistic function. The proposed method has been applied for equalizing 5 images with a different resolution and histogram distribution. The experimental results show that the proposed method has the superior enhancement performances compared with the source images and the traditional global histogram equalization, respectively.

Comparison of Based on Histogram Equalization Techniques by Using Normalization in Thoracic Computed Tomography (흉부 컴퓨터 단층 촬영에서 정규화를 사용한 다양한 히스토그램 평준화 기법을 비교)

  • Lee, Young-Jun;Min, Jung-Whan
    • Journal of radiological science and technology
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    • v.44 no.5
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    • pp.473-480
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    • 2021
  • This study was purpose to method that applies for improving the image quality in CT and X-ray scan, especially in the lung region. Also, we researched the parameters of the image before and after applying for Histogram Equalization (HE) such as mean, median values in the histogram. These techniques are mainly used for all type of medical images such as for Chest X-ray, Low-Dose Computed Tomography (CT). These are also used to intensify tiny anatomies like vessels, lung nodules, airways and pulmonary fissures. The proposed techniques consist of two main steps using the MATLAB software (R2021a). First, the technique should apply for the process of normalization for improving the basic image more correctly. In the next, the technique actively rearranges the intensity of the image contrast. Second, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method was used for enhancing small details, textures and local contrast of the image. As a result, this paper shows the modern and improved techniques of HE and some advantages of the technique on the traditional HE. Therefore, this paper concludes that various techniques related to the HE can be helpful for many processes, especially image pre-processing for Machine Learning (ML), Deep Learning (DL).