• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.310-315
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• 2004

Image taken in the night can be low-contrast images because of poor environment and image transmission. We propose an algorithm that improves the acquired low-contrast image. MPEG-2 separates chrominance and illuminance, and compresses respectively because human vision is more sensitive to luminance. We extracted illumination and used K-means algorithm to find a proper crossover point automatically. We used K-means algorithm in the viewpoint that the problem of crossover point selection can be considered as the two-category classification problem. We divided an image into two subimages using the crossover point, and applied the histogram equalization method respectively. We used the index of fuzziness to evaluate the degree of improvement. We compare the results of the proposed method with those of other methods.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.837-847
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• 2016

Tone mapping for High Dynamic Range(HDR) image provides matching human visual perception between real world scene and displayable devices. Recently, a tone mapping algorithm based on localized gamma correction is proposed. This algorithm is using human visual properties of contrast and colorfulness with background intensity, generating a weight map for gamma correction. However, this method have limitations of controlling enhancement region as well as generating halo artifacts caused by the weight map construction. To overcome aforementioned limitations, proposed algorithm in this paper modifies previous weight map, considering base layer intensity of input luminance channel. By determining enhancement region locally and globally based on base layer intensity, gamma values are corrected accordingly. Therefore, proposed algorithm selectively enhances local brightness and controls strength of edges. Subjective evaluation using z-score shows that our proposed algorithm outperforms the conventional methods.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.629-638
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• 2003

In this paper, we propose an improved method of watermarking to increase the invisibility of a watermark and robustness against attacks for the purpose of removing the embedded watermark. The proposed method adaptively selects DCT blocks and determines position and intensity for watermarking based on the characteristics of human visual system. The used features are texture, luminance and contrast. We show the experimental results against image processing attacks such as cropping, image enhancement, low pass filtering, and JPEG compression, and then discuss the performance of the proposed method.

• Korean Journal of Materials Research
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• v.13 no.4
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• pp.271-278
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• 2003

Conventional backlight for liquid crystal display (LCD) uses mercury which leads to environmental pollution. In this study, characteristics of AC coplanar type mercury-free plasma flat lamp have been studied. Pollution-free Xe-He is adopted as a discharge gas system. Since the Xe gas has a lower efficiency in generating vacuum ultraviolet (VUV) than mercury, the improvement of luminance and luminous efficiency in the Xe gas system is very important. The electrode, dielectric, and phosphor layers constituting lamp are formed on the bottom glass by the screen printing method. The effects of pulse shape, on-time, and pulse frequency on the luminance and luminous efficiency have been examined. For Xe(5%)-He gas, the lamp exhibits higher efficiency with sharper pulse shape, higher peak voltage, and shorter pulse on-time (up to 2 $\mu\textrm{s}$). Higher efficiency and lower consumption of power were obtained at 30 kHz than at 60 kHz. The collision of ion to bottom electrodes is a dominant factor to raise the lamp temperature. Therefore the high voltage and low current discharge system is necessary for reduction of the lamp temperature as well as for enhancement of the luminous efficiency.

• Proceedings of the KIEE Conference
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• 2006.07c
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• pp.1293-1294
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• 2006

OLEDs based on organic thin films are similar to semiconductor base light-emitting diodes in that they were also considered to be one of the next generation flat-panel displays. They are attractive because of low-operating voltage, low power consumption, ease of fabrication, and low cost. In this study, we used poly (3,4-ethylenedioxythiophene)/poly (4-styrenesulfonate) (PE DOT : PSS) as a hole injection layer. In this experiment spin coating method was used with various speed rate. The fundamental structure of the OLEDs was ITO/PEDOT:PSS/NPB/$Alq_3$/Al. As a result, we obtained the enhancement performance of OLEDs when the spin coating speed was 4000 rpm. We obtained a maximum luminance of 24334 $cd/m^2$ at a current density of 967 $mA/cm^2$.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.263-270
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• 2009

Histogram Equalization (HE) is a very popular technique for enhancing the contrast of an image. HE stretches the dynamic range of an image using the cumulative distribution function of a given input image, therefore improving its contrast. However, HE has a well-known problem : when HE is applied for the contrast enhancement, there is a significant change in brightness. To resolve this problem, we propose An Adaptive Contrast Enhancement Algorithm using Subhistogram Area-Ratioed Histogram Redistribution, a new method that helps reduce excessive contrast enhancement. This proposed algorithm redistributes the dynamic range of an input image using its mean luminance value and the ratio of sub-histogram area. Experimental results show that by this redistribution, the significant change in brightness is reduced effectively and the output image is able to preserve the naturalness of an original image even if it has a poor histogram distribution.

• Journal of Ocean Engineering and Technology
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• v.36 no.1
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• pp.32-40
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• 2022

Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.51-60
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• 2024

Aiming at the problem of low contrast and difficult to recognize video images in low-light environment, we propose an adaptive contrast compensation enhancement algorithm based on human visual perception. First of all, the video image characteristic factors in low-light environment are extracted: AL (average luminance), ABWF (average bandwidth factor), and the mathematical model of human visual CRC(contrast resolution compensation) is established according to the difference of the original image's grayscale/chromaticity level, and the proportion of the three primary colors of the true color is compensated by the integral, respectively. Then, when the degree of compensation is lower than the bright vision precisely distinguishable difference, the compensation threshold is set to linearly compensate the bright vision to the full bandwidth. Finally, the automatic optimization model of the compensation ratio coefficient is established by combining the subjective image quality evaluation and the image characteristic factor. The experimental test results show that the video image adaptive enhancement algorithm has good enhancement effect, good real-time performance, can effectively mine the dark vision information, and can be widely used in different scenes.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.63-73
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• 2007

This paper proposes an illuminant-adaptive reproduction method using light adaptation and flare conditions for a mobile display. Displayed images in daylight are perceived as quite dark due to the light adaptation of the human visual system, as the luminance of a mobile display is considerably lower than that of an outdoor environment. In addition, flare phenomena decrease the color gamut of a mobile display and de-saturating the chroma. Therefore, this paper presents an enhancement method composed of lightness enhancement and chroma compensation. First, the ambient light intensity is measured using a lux-sensor, then the flare is calculated based on the reflection ratio of the display device and the ambient light intensity. To improve the perceived image, the image's luminance is transformed by linearization of the response to the input luminance according to the ambient light intensity. Next, the displayed image is compensated according to the physically reduced chroma, resulting from flare phenomena. This study presents a color reproduction method based on an inverse cone response curve and flare condition. Consequently, the proposed algorithm improves the quality of the perceived image adaptive to an outdoor environment.

• Journal of IKEEE
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• v.25 no.1
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• pp.139-147
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• 2021

This paper introduces an image decomposition-based deep learning method and loss function to improve low-light images. In order to remove color distortion and halo artifact, illuminance channel of an input image is decomposed into reflectance and luminance channels, and a decomposition-based multiple structural deep learning process is applied to each channel. In addition, a mixed norm-based loss function is described to increase the stability and remove blurring in reconstructed image. Experimental results show that the proposed method effectively improve various low-light images.