• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1528-1536
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• 2014

Although the process about korea image restoration contents program development and liberalization have cultural and artistic worth, it is not possible to restore technically or cost enormous expense. In the study, it suggest the needs of korea image restoration contents program development through image restoration case. It is revitalize that the study in digital image restoration technical prepare the ground for passing high-qualities cultural legacy by restoring classical film. We think the development of korea image restoration software will strengthen the basis to the new creation industry of high value-added in the global cultural prosperity industry.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.121-125
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• 2008

According to the development of software and hardware about multimedia technologies, images are used to store information extracted from data. Noises by various causes, however, are added in the process of forming images, recording and transmitting in ubiquitous environments. In image restoration viewpoints to remove them. appropriate filtering methodologies, wiener of mean etc, are utilized. Various ways for image restoration are studied as well. Therefore, in this paper, we Propose user defined image restoration that applies the most appropriate parameters for image restoration and show the implementation result of the system using various parameters including mean filter and wiener filter to advance quality of degraded source image affected by noise in ubiquitous environment and medical fields.

• Journal of information and communication convergence engineering
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• v.14 no.1
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• pp.57-63
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• 2016

In generally, the resolution of reconstructed three-dimensional images can be seriously degraded by undesired occlusions in the integral imaging system, because the undesired information of the occlusion overlap the three-dimensional images to be reconstructed. To solve the problem of the undesired occlusion, we present an exemplar-based image restoration method in integral imaging system. In the proposed method, a minimum spanning tree-based stereo matching method is used to remove the region of undesired occlusions in each elemental image. After that, the removed occlusion region of each elemental images are re-established by using the exemplar-based image restoration method. For further improve the performance of the image restoration, the structure tensor is used to solve the filling error cause by discontinuous structures. Finally, the resolution enhanced three-dimensional images are reconstructed by using the restored elemental images. The preliminary experiments are presented to demonstrate the feasibility of the proposed method.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.862-867
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• 2006

To restore image degraded by blur and additive noise in the optical and electrical system, a regularized iterative restoration is used. A regularization operator is usually applied to all over the image without considering the local characteristics of image in conventional method. As a result, ringing artifacts appear in edge regions and the noise is amplified in flat regions. To solve these problems we propose an adaptive regularization iterative restoration considering the characteristic of edge and flat regions using directional regularization operator. Experimental results show that the proposed method suppresses the noise amplification in flat regions, and restores the edge more sharply in edge regions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.172-179
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• 2015

Because of the development of computers and high-technology applications, all devices that we use have become more intelligent. In recent years, security and surveillance systems have become more complicated as well. Before new technologies included video surveillance systems, security cameras were used only for recording events as they occurred, and a human had to analyze the recorded data. Nowadays, computers are used for video analytics, and video surveillance systems have become more autonomous and automated. The types of security cameras have also changed, and the market offers different kinds of cameras with integrated software. Even though there is a variety of hardware, their capabilities leave a lot to be desired. Therefore, this drawback is trying to compensate by dint of computer program solutions. Image processing is a very important part of video surveillance and security systems. Capturing an image exactly as it appears in the real world is difficult if not impossible. There is always noise to deal with. This is caused by the graininess of the emulsion, low resolution of the camera sensors, motion blur caused by movements and drag, focus problems, depth-of-field issues, or the imperfect nature of the camera lens. This paper reviews image processing, pattern recognition, and image digitization techniques, which will be useful in security services, to analyze bio-images, for image restoration, and for object classification.

• Journal of the Korea Computer Graphics Society
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• v.24 no.2
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• pp.1-9
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• 2018

In this paper, we propose a method to restore corrupted black and white facial images to color. Previous studies have shown that when coloring damaged black and white photographs, such as old ID photographs, the area around the damaged area is often incorrectly colored. To solve this problem, this paper proposes a method of restoring the damaged area of input photo first and then performing colorization based on the result. The proposed method consists of two steps: BEGAN (Boundary Equivalent Generative Adversarial Networks) model based restoration and CNN (Convolutional Neural Network) based coloring. Our method uses the BEGAN model, which enables a clearer and higher resolution image restoration than the existing methods using the DCGAN (Deep Convolutional Generative Adversarial Networks) model for image restoration, and performs colorization based on the restored black and white image. Finally, we confirmed that the experimental results of various types of facial images and masks can show realistic color restoration results in many cases compared with the previous studies.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.35-40
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• 2019

In this paper, we propose a technique to reconstruct the iris image from the iris code by analyzing the process of generating the iris code and calculating it inversely. Iris recognition is an authentication method for authenticating an individual's identity by using iris information of an eye having unique information of an individual. The iris recognition extracts the features of the iris from the iris image, creates the iris code, and determines whether to authenticate using the corresponding code. The iris recognition method using the iris code is a method proposed by Daugman for the first time and is widely used as a representative method of iris recognition technology currently used commercially. In this paper, we restore the iris image with only the iris code, and test whether the reconstructed image and the original image can be recognized, and analyze restoration vulnerability of Daugman's iris code.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.953-960
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• 2007

To correctly detect muras, visual defects in TFT-LCD panels, image distortion occurring on the profess of capturing panels should be corrected. In general vision systems, there are several known methods to restore the observed image. However, the vignetting effect particularly shown only in panel images cannot be easily restored through traditional methods because it is combined with background non-uniformity due to the unique characteristic of panel. To increase the reliability of image restoration, the vignetting effect should be properly corrected after being separated from image background. Therefore, in this paper we present a new method to analyze and correct the vignetting effect of panel images using principal component analysis. Experimental results for a total of 175 test images showed that the average contrast error of the muras in the distorted images was reduced from 37% to 11% and the mura misidentification rate was decreased from 14.8% to 2.2% by image restoration.

• Journal of the Korea Computer Graphics Society
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• v.24 no.3
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• pp.43-48
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• 2018

In this paper, we present a method for removing shadows from scanned documents. Compared to the existing methods such as one based on image pyramid representation or adaptive thresholding, our method produces more robust and higher quality results. The basic idea of the approach is to use the local image statistics and to separate interesting regions from the image such as the regions around letters and figures. For the separated regions, we adaptively adjust the local brightness and contrast, and apply the sigmoid function to the intensity values as well to enhance the clarity of the image. For separated the other empty regions, we apply the gradient-base image hole filling method to fill the region with smooth color change.

• Imaging Science in Dentistry
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• v.50 no.4
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• pp.331-337
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• 2020

Purpose: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. Materials and Methods: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. Results: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. Conclusion: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.