• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.525-532
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• 2018

Recently, due to the advancements of sensor network technologies and camera technologies, there are increasing needs to effectively monitor the environment in a region that is difficult to access by using the visual sensor network that combines these two technologies. Since the image captured by the visual sensor reflects the natural phenomenon as it is, the quality of the image may deteriorate depending on the weather or time. In this paper, we propose an algorithm to improve the contrast of images using the characteristics of images obtained from visual sensors. In the proposed method, we first set the region of interest and then analyzes the change of the color value of the region of interest according to the brightness value of the image. The contrast of an image is improved by using the high contrast image of the same object and the analysis information. It is shown by experimental results that the proposed method improves the contrast of an image by restoring the color components of the low contrast image simply and accurately.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.837-856
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• 2016

Underwater image enhancement has received considerable attention in last decades, due to the nature of poor visibility and low contrast of underwater images. In this paper, we propose a new automatic underwater image enhancement algorithm, which combines nonsubsampled contourlet transform (NSCT) domain enhancement techniques with the mechanism of the human visual system (HVS). We apply the multiscale retinex algorithm based on the HVS into NSCT domain in order to eliminate the non-uniform illumination, and adopt the threshold denoising technique to suppress underwater noise. Our proposed algorithm incorporates the luminance masking and contrast masking characteristics of the HVS into NSCT domain to yield the new HVS-based NSCT. Moreover, we define two nonlinear mapping functions. The first one is used to manipulate the HVS-based NSCT contrast coefficients to enhance the edges. The second one is a gain function which modifies the lowpass subband coefficients to adjust the global dynamic range. As a result, our algorithm can achieve contrast enhancement, image denoising and edge sharpening automatically and simultaneously. Experimental results illustrate that our proposed algorithm has better enhancement performance than state-of-the-art algorithms both in subjective evaluation and quantitative assessment. In addition, our algorithm can automatically achieve underwater image enhancement without any parameter tuning.

• Journal of radiological science and technology
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• v.40 no.3
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• pp.393-400
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• 2017

The purpose of this study is to suggest a method to reduce the dose by Analyzing the dose area product (DAP) and image quality according to the change of tube current using NEMA Phantom. The spatial resolution and low contrast resolution were used as evaluation criteria in addition to signal to noise ratio (SNR) and contrast to noise ratio (CNR), which are important image quality parameters of intervention. Tube voltage was fixed at 80 kVp and the amount of tube current was changed to 20, 30, 40, and 50 mAs, and the dose area product and image quality were compared and analyzed. As a result, the dose area product increased from $1066mGycm^2$ to $6160mGycm^2$ to 6 times as the condition increased, while the spatial resolution and low contrast resolution were higher than 20 mAs and 30 mAs, Spatial resolution and low contrast resolution were observed below the evaluation criteria. In addition, the SNR and CNR increased up to 30 mAs, slightly increased at 40 mAs, but not significantly different from the previous one, and decreased at 50 mAs. As a result, the exposure dose significantly increased due to overexposure of the test conditions and the image quality deteriorated in all areas of spatial resolution, low contrast resolution, SNR and CNR.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.87-96
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• 2019

Improvement of low-light image mainly focuses on the contrast enhancement. Many researches have been carried out for brightness enhancement, contrast improvement and illumination reduction. Recently, the aforementioned approaches have been replaced by artificial neural networks. This paper proposes a methodology that can replace the Retinex-based reflectance image acquisition by deep neural network. Experiments carried out on 102 low-light images validated the feasibility of the replacement by producing PSNR=30.8682(db) and SSIM=0.4345.

• Korean Journal of Digital Imaging in Medicine
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• v.12 no.2
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• pp.127-132
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• 2010

In this study, the correlation among the changes of Modulation Transfer Function(MTF) in the noise and high-contrast resolution and the change of Contrast to noise ratio(CNR) in the low-contrast resolution will be examined to investigate the estimation of image quality according to the type of algorithms. The image data obtained by scanning American Association of Physicists in Medicine(AAPM) phantom was applied to each algorithm and the exposure condition of 120 kVp, 250 mAs, and then the CT number and noise were measured. The MTF curved line of the high-contrast resolution was calculated with Point Spread Function(PSF) by using the analysis program by Philips, resulting in 0.5 MTF, 0.1 MTF and 0.02 MTF respectively. The low-contrast resolution was calculated with CNR and the uniformity was measured to each algorithm. Since the measurement value for the uniformity of the equipment was below ${\pm}$ 5 HU, which is the criterion figure, it was found to belong to the normal range. As the algorithm got closer from soft to edge, the standard deviation of CT number increased, which indicates that the noise increased as well. As for MTF, 0.5 MTF, 0.1 MTF and 0.02 MTF were all sharp algorithms, and as the algorithm got closer from soft to edge, it was possible to distinguish more clearly with the naked eye. On the other hand, CNR gradually decreased, because the difference between the contrast hole CT number and the acrylic CT number was the same while the noise of hole increased.

• Journal of the Korean Society of Radiology
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• v.9 no.5
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• pp.301-306
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• 2015

X-ray can be applied to obtain a projection image of an object. It is not easy to obtain an high quality image for the object composed of low and high density materials. For the object with large difference in density, it is possible to realize high contrast image using images of low and high tube voltages and image processing. The plastic and metalic parts of the electronic components can be imaged by the dual energy technique which use low and high tube voltages and by processing pixel-by-pixel using visual C++. The contrast-enhanced image can be used to detect and observe defects within the electronic components.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.361-362
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• 2007

We propose a contrast-controlled feature detection approach for steel radiograph image. X-ray images are low contrast, dark and high noise image. So, It is not simple to detect defects directly in automated radiography inspection system. Contrast enhancement, histogram equalization and median filter are the most frequently used techniques to enhance the X-ray images. In this paper, the adaptive control method based on contrast limited histogram equalization is compared with several histogram techniques. Through comparative analysis, CLAHE(contrast controlled adaptive histogram equalization) can enhance detection of defects better.

• Journal of the Optical Society of Korea
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• v.17 no.1
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• pp.86-90
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• 2013

Various methods have been proposed for enhancing the contrast of laser speckle contrast image (LSCI) in subcutaneous blood flow measurements. However, the LSCI still suffers from low image contrast due to tissue turbidity. Herein, a physicochemical tissue optical clearing (PCTOC) method was employed to enhance the contrast of LSCI. Ex vivo and in vivo experiments were performed with porcine skin samples and male ICR mice, respectively. The ex vivo LSCIs were obtained before and 90 min after the application of the PCTOC and in vivo LSCIs were obtained for 60 min after the application of the PCTOC. In order to obtain the skin recovery images, saline was applied for 30 min after the application of the PCTOC was completed. The visible appearance of the tubing under ex vivo samples and the in vivo vasculature gradually enhanced over time. The LSCI increased as a function of time after the application of the PCTOC in both ex vivo and in vivo experiments, and properly recovered to initial conditions after the application of saline in the in vivo experiment. The LSCI combined with the PCTOC was greatly enhanced even in deep vasculature. It is expected that similar results will be obtained in in vivo human studies.

• Journal of radiological science and technology
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• v.39 no.1
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• pp.71-79
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• 2016

The purpose of this study is computed tomography contrast agent at low concentrations and low tube voltage technique to evaluate the usefulness on the phantom image. By varying the degree of mixture by the contrast medium concentration it was inserted in phantom. It was taken by changing the tube voltage and tube current step by step, and to evaluate the dose and the CT value obtained from the phantom image. As a result, low-contrast, low tube voltage(300 mgI/ml, 100 kV) was reduced by an average 21%(CTDIvol; computed tomography dose indexvol) more standard condition(350 mgI/ml, 120 kV). SNR was increased at all depths of the phantom, respectively 1:10 and 1:20(by diluting a contrast agent and normal saline) 12.2(26%) 6.2(17%). CNR was increased at all depths of the phantom, respectively 1:10 and 1:20(by diluting a contrast agent and normal saline) 11.5(32%), 6.3(26%). Research work on the CT scan is necessary in a variety of studies on the low contrast concentration and low tube voltage techniques for dose reduction and reducing of side effects the contrast agent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2749-2763
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• 2021

The desirable result of infrared (IR) and visible (VIS) image fusion should have textural details from VIS images and salient targets from IR images. However, detail information in the dark regions of VIS image has low contrast and blurry edges, resulting in performance degradation in image fusion. To resolve the troubles of fuzzy details in dark regions of VIS image fusion, we have proposed a method of reflectance estimation for IR and VIS image fusion. In order to maintain and enhance details in these dark regions, dark region approximation (DRA) is proposed to optimize the Retinex model. With the improved Retinex model based on DRA, quasi-Newton method is adopted to estimate the reflectance of a VIS image. The final fusion outcome is obtained by fusing the DRA-based reflectance of VIS image with IR image. Our method could simultaneously retain the low visibility details in VIS images and the high contrast targets in IR images. Experiment statistic shows that compared to some advanced approaches, the proposed method has superiority on detail preservation and visual quality.