• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5998-6016
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• 2017

In thin-film transistor liquid-crystal displays (TFT-LCDs), which are most commonly used in mobile devices, the backlight accounts for about 70% of the power consumption. Therefore, most low-power-related studies focus on realizing power savings through backlight dimming. Image compensation is performed to mitigate the visual distortion caused by the backlight dimming. Therefore, popular techniques include pixel compensation for brightness recovery and contrast enhancement, such as histogram equalization. However, existing pixel compensation techniques often have limitations with respect to blur owing to the pixel saturation phenomenon, or because contrast enhancement cannot adequately satisfy the human visual system (HVS). To overcome these, in this study, we propose a novel dimming technique to achieve both power saving and HVS-awareness by combining the pixel compensation and histogram specifications, which convert the original cumulative density function (CDF) by designing and using the desired CDF of an image. Because the process of obtaining the desired CDF is customized to consider image characteristics, histogram specification is found to achieve better HVS-awareness than histogram equalization. For the experiments, we employ the LIVE image database, and we use the structural similarity (SSIM) index to measure the degree of visual satisfaction. The experimental results show that the proposed technique achieves up to 15.9% increase in the SSIM index compared with existing dimming techniques that use pixel compensation and histogram equalization in the case of the same low-power ratio. Further, the results indicate that it achieves improved HVS-awareness and increased power saving concurrently compared with previous techniques.

• Journal of Broadcast Engineering
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• v.15 no.3
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• pp.332-345
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• 2010

A unified image enhancement method is proposed for high-resolution image which based on color constancy and histogram equalization using edge region. To speed up the method, smaller image is used when parameters of color constancy and histogram equalization are determined. In the color constancy process, nth-derivative of gaussian is applied to x and y axis separately in order to estimate the color of the illumination rapidly. In the histogram equalization process, the histogram obtained from near-edge region is used for the histogram equalization. In the experiments, high-resolution images taken by digital camcorder are used for verifying the performance of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.293-298
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• 2011

In this paper, we propose a new image descriptor, that is, a rotation invariant histogram of oriented gradients (RIHOG). RIHOG overcomes a disadvantage of the histogram of oriented gradients (HOG), which is very sensitive to image rotation. The HOG only uses magnitude values of a pixel without considering neighboring pixels. The RIHOG uses the accumulated relative magnitude values of corresponding relative orientation calculated with neighboring pixels, which has an effect on reducing the sensitivity to image rotation. The performance of RIHOG is verified via the index of classification and classification of Brodatz texture data.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1093-1096
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• 1999

In this paper, video image segmentation algorithm based on color histogram and change detector is proposed. Color histograms are calculated from both changed region which is detected in the previous and current frame and unchanged region. With each histogram, modes and valleys are detected. Then, color vectors are calculated by averaging pixels in modes. Markers are extracted by labeling color vectors that represent modes, the watershed algorithm is applied to determine uncertain region. In growing region, the root mean square(RMS) of the distance between average pixel in marker region and adjacent pixel is used as a measure. The proposed algorithm based on color histogram and change detector segments video image fastly and effectively. And simulation results show that the proposed method determines the exact boundary between background and foreground.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.39-47
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• 2016

In this paper, we propose an approach for real time computation of rayleigh CLAHE using a FPGA. The contrast enhancement technique should be applied in thermal equipment having a low contrast image. And thermal equipment must be processed in real time. The CLAHE is an improved algorithm based Histogram Equalization, but the HW design is complex. A value greater than a given threshold in CLAHE should be equally distributed on the other histogram bin, this process requires iterations for the distribution. But implementation of this processing in the FPGA is constrained, so this section was implemented on the assumption of the histogram distribution or modified the operation process or implemented separately in the CPU. In this paper, we designed a distinct redistribution operation in two stages. So FPGA was designed for easy, this was designed to be distributed evenly without the assumptions and constraints. In addition, we have designed a CLAHE with the rayleigh distribution to the FPGA. The simulation shows that the proposed method provides a better image quality in the thermal image.

• 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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.193-198
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• 2010

In this paper, an efficient contrast enhancement algorithm using histogram stretching is proposed. Histogram equalization (HE) and histogram stretching (HS) are effective techniques for contrast enhancement. However, HE and HS result often in excessive contrast enhancement. Proposed technique not only produces better results than those of conventional contrast enhancement techniques, but is also adaptively adjusted to image contents.

• 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.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.646-651
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• 2014

Histogram equalization generally has the disadvantage that if the distribution of the gray level of an image is concentrated in one place, then the range of the gray level in the output image is excessively expanded, which then produces a visually unnatural result. However, a gamma transformation can reduce such unnatural appearances since it operates under a nonlinear regime. Therefore, this paper proposes a new histogram equalization method that can improve image quality by using a gamma transformation. The proposed method 1) derives the proper form of the gamma transformation by using the average brightness of the input image, 2) linearly combines the earlier gamma transformation with a CDF (Cumulative Distribution Function) for the image in order to obtain a new CDF, and 3) to finally perform histogram equalization by using the new CDF. The experimental results show that relative to existing methods, the proposed method provides good performance in terms of quantitative measures, such as entropy, UIQ, SSIM, etc., and it also naturally enhances the image quality in visual perspective as well.

• Journal of Biomedical Engineering Research
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• v.42 no.3
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• pp.80-85
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• 2021

We developed a model to classify the absence of cervical cancer using deep learning from the cervical image to which the histogram equalization algorithm was applied, and to compare the performance of each model. A total of 4259 images were used for this study, of which 1852 images were normal and 2407 were abnormal. And this paper applied Image Sharpening(IS), Histogram Equalization(HE), and Contrast Limited Adaptive Histogram Equalization(CLAHE) to the original image. Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity index for Measuring image quality(SSIM) were used to assess the quality of images objectively. As a result of assessment, IS showed 81.75dB of PSNR and 0.96 of SSIM, showing the best image quality. CLAHE and HE showed the PSNR of 62.67dB and 62.60dB respectively, while SSIM of CLAHE was shown as 0.86, which is closer to 1 than HE of 0.75. Using ResNet-50 model with transfer learning, digital image-processed images are classified into normal and abnormal each. In conclusion, the classification accuracy of each model is as follows. 90.77% for IS, which shows the highest, 90.26% for CLAHE and 87.60% for HE. As this study shows, applying proper digital image processing which is for cervical images to Computer Aided Diagnosis(CAD) can help both screening and diagnosing.