• 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 the Korea Society of Computer and Information
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• v.12 no.3
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• pp.131-136
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• 2007

This paper proposes a new approach to eliminate the reflectance component for the detection of text in color images. Color images, printed by color printing technology, normally have an illumination component as well as a reflectance component. It is well known that a reflectance component usually obstructs the task of detecting and recognizing objects like texts in the scene, since it blurs out an overall image. We have developed an approach that efficiently removes reflectance components while preserving illumination components. We decided whether an input image hits Normal or Polarized for determining the light environment, using the histogram which consisted of a red component. We were able to go ahead through the ability to extract by reducing the blur phenomenon of text by light because reflection component by an illumination change and removed it and extracted text. The experimental results have shown a superior performance even when an image has a complex background. Text detection and recognition performance is influenced by changing the illumination condition. Our method is robust to the images with different illumination conditions.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.281-291
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• 2019

Depth from defocus estimates the 3D depth by using a phenomenon in which the object in the focal plane of the camera forms a clear image but the object away from the focal plane produces a blurred image. In this paper, algorithms are studied to estimate 3D depth by analyzing the degree of blur of the image taken with a single camera. The optimized object range was obtained by 3D depth estimation derived from depth from defocus using one image of a single camera or two images of different focus of a single camera. For depth estimation using one image, the best performance was achieved using a focal length of 250 mm for both smartphone and DSLR cameras. The depth estimation using two images showed the best 3D depth estimation range when the focal length was set to 150 mm and 250 mm for smartphone camera images and 200 mm and 300 mm for DSLR camera images.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.351-360
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• 2022

Image blurring is a phenomenon that occurs due to factors such as movement of a subject and shaking of a camera. Recently, the research for image deblurring has been actively conducted based on convolution neural networks. In particular, the method of guiding the restoration process via the difference between blur and sharp images has shown the promising performance. This paper proposes a novel method for improving the deblurring performance based on the distortion information. To this end, the transformer-based neural network module is designed to guide the restoration process. The proposed method efficiently reflects the distorted region, which is predicted through the global inference during the deblurring process. We demonstrate the efficiency and robustness of the proposed module based on experimental results with various deblurring architectures and benchmark datasets.

• Journal of the Korean Society of Radiology
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• v.16 no.6
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• pp.673-680
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• 2022

In this study, based on PLA, we analyzed the Hounsfield Unit (HU) of materials containing 20% each of aluminum, wood, copper, carbon, and marble, and tried to analyze how they affect the image. A cylindrical phantom of 5×30×30 ㎣ (thickness×diameter×height) was fabricated using a entry-level 3D printer. The kV was changed to 80, 100 and 120, and the mAs was changed to 100 and 200 mAs, and the phantom in the center of the table was cross-scanned under a total of six conditions. A circular ROI was set using image J program and the quantification value of the material part HU and the quantification value of the peripheral part CNR were obtained. The HU average of the material part increased in the order of [PLA - wood 20%], [PLA - marble 20%], [PLA - carbon 20%], [PLA 100%], [PLA - aluminum 20%], [PLA - copper 20%] (p<0.05) a negative correlation was confirmed with the HU by increasing kV. It was confirmed that the CNR value in the peripheral area increased in the order of [PLA - marble 20%], [PLA - copper 20%], [PLA - carbon 20%], [PLA - wood 20%], [PLA - aluminum 20%], and [PLA - 100%] (p<0.05). Human organs with similar HU values for each material are [PLA - copper 20%] compact bone, [PLA - aluminum 20%] cancellous bone, [PLA 100%] coagulated blood, [PLA - carbon 20%] and [PLA - marble 20%] liver, muscle, spleen and [PLA - wood 20%] had similar values to fat. In addition, we confirmed the blur phenomenon that blurs the image around the filament with all materials, and confirmed that [PLA 100%] especially has the most blur around the filament. Therefore, it is considered desirable to reflect the HU value of the target organ and consider cloudiness around the phantom when selecting materials for medical phantom fabrication, and this research can provide basic data.