• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.767-774
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• 2002

Conventional research on image restoration has focused on restoring degraded images resulting from image formation, storage and communication, mainly in the signal processing field. Related research on recovering original image information of caption regions includes a method using BMA(block matching algorithm). The method has problem with frequent incorrect matching and propagating the errors by incorrect matching. Moreover, it is impossible to recover the frames between two scene changes when scene changes occur more than twice. In this paper, we propose a method for recovering original images using EBMA(Extended Block Matching Algorithm) and a region compensation method. To use it in original image recovery, the method extracts a priori knowledge such as information about scene changes, camera motion and caption regions. The method decides the direction of recovery using the extracted caption information(the start and end frames of a caption) and scene change information. According to the direction of recovery, the recovery is performed in units of character components using EBMA and the region compensation method. Experimental results show that EBMA results in good recovery regardless of the speed of moving object and complexity of background in video. The region compensation method recovered original images successfully, when there is no information about the original image to refer to.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.113-122
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• 2000

It is sometimes necessary to remove the captions and recover original images from video images already broadcast, When the number of images requiring such recovery is small, manual processing is possible, but as the number grows it would be very difficult to do it manually. Therefore, a method for recovering original image for the caption areas in needed. Traditional research on image restoration has focused on restoring blurred images to sharp images using frequency filtering or video coding for transferring video images. This paper proposes a method for automatically recovering original image using BMA(Block Matching Algorithm). We extract information on caption regions and scene change that is used as a prior-knowledge for recovering original image. From the result of caption information detection, we know the start and end frames of captions in video and the character areas in the caption regions. The direction for the recovery is decided using information on the scene change and caption region(the start and end frame for captions). According to the direction, we recover the original image by performing block matching for character components in extracted caption region. Experimental results show that the case of stationary images with little camera or object motion is well recovered. We see that the case of images with motion in complex background is also recovered.

• Journal of Biomedical Engineering Research
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• v.19 no.2
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• pp.119-128
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• 1998

This paper describes a filtering algorithm which employs apriori information of SPECT lesion detectability potential for the filtering of degraded projection images prior to the backprojection reconstruction. In this algorithm, we determined m minimum detectable lesion sized(MDLSs) by assuming m object contrasts uniformly-chosen in the range of 0.0-1.0, based on a signal/noise model which provides the capability potential of SPECT in terms of physical factors. A best estimate of given projection image is attempted as a weighted combination of the subimages from m optimal filters whose design is focused on maximizing the local S/N ratios for the MDLS-lesions. These subimages show relatively larger resolution recovery effect and relatively smaller noise reduction effect with the decreased MDLS, and the weighting on each subimage was controlled by the difference between the subimage and the maximum-resolution-recovered projection image. The proposed filtering algoritym was tested on SPECT image reconstruction problems, and produced good results. Especially, this algorithm showed the adaptive effect that approximately averages the filter outputs in homogeneous areas and sensitively depends on each filter strength on contrast preserving/enhancing in textured lesion areas of the reconstructed image.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.21-29
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• 2024

Reducing the radiation dose during CT scanning can lower the risk of radiation exposure, but not only does the image resolution significantly deteriorate, but the effectiveness of diagnosis is reduced due to the generation of noise. Therefore, noise removal from CT images is a very important and essential processing process in the image restoration. Until now, there are limitations in removing only the noise by separating the noise and the original signal in the image area. In this paper, we aim to effectively remove noise from CT images using the wavelet transform-based GAN model, that is, the WT-GAN model in the frequency domain. The GAN model used here generates images with noise removed through a U-Net structured generator and a PatchGAN structured discriminator. To evaluate the performance of the WT-GAN model proposed in this paper, experiments were conducted on CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. As a result of the performance experiment, the WT-GAN model is better than the traditional filter, that is, the BM3D filter, as well as the existing deep learning models, such as DnCNN, CDAE model, and U-Net GAN model, in qualitative and quantitative measures, that is, PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure) showed excellent results.

• Restorative Dentistry and Endodontics
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• v.26 no.4
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• pp.263-272
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• 2001

The aim of this study was to investigate the influence of four different light curing modes on the marginal leakage of Class V composite resin restoration. Eighty extracted human premolars were used. Wedge-shaped class Y cavities were prepared on the buccal surface of the tooth with high-speed diamond bur without bevel. The cavities were positioned half of the cavity above and half beyond the cemento-enamel junction. The depth, height, and width of the cavity were 2 mm, 3 mm and 2 mm respectively. The specimens were divided into 4 groups of 20 teeth each. All the specimen cavities were treated with Prime & Bond$^{R}$ NT dental adhesive system (Dentsply DeTrey GmbH, Germany) according to the manufacturer's instructions and cured for 10 seconds except group VI which were cured for 3 seconds. All the cavities were restored with resin composite Spectrum$^{TM}$ TPH A2 (Dentsply DeTrey GmbH, Germany) in a bulk. Resin composites were light-cured under 4 different modes. A regular intensity group (600 mW/${cm}^2$, group I) was irradiated for 30 s, a low intensity group (300 mW/${cm}^2$, group II) for 60 s and a ultra-high intensity group (1930 mW/${cm}^2$, group IV) for 3 s. A pulse-delay group (group III) was irradiated with 400 mW/${cm}^2$ for 2 s followed by 800 mW/${cm}^2$ for 10 s after 5 minutes delay. The Spectrum$^{TM}$ 800 (Dentsply DeTrey GmbH, Germany) light-curing units were used for groups I, II and III and Apollo 95E (DMD, U.S.A.) was used for group IV. The composite resin specimens were finished and polished immediately after light curing except group III which were finished and polished during delaying time. Specimens were stored in a physiologic saline solution at 37$^{\circ}C$ for 24 hours. After thermocycling (500$\times$, 5-55$^{\circ}C$), all teeth were covered with nail varnish up to 0.5 mm from the margins of the restorations, immersed in 37$^{\circ}C$, 2% methylene blue solution for 24 hours, and rinsed with tap water for 24 hours. After embedding in clear resin, the specimens were sectioned with a water-cooled diamond saw (Isomet$^{TM}$, Buehler Co., Lake Bluff, IL, U.S.A.) along the longitudinal axis of the tooth so as to pass the center of the restorations. The cut surfaces were examined under a stereomicroscope (SZ-PT Olympus, Japan) at ${\times}$25 magnification, and the images were captured with a CCD camera (GP-KR222, Panasonic, Japan) and stored in a computer with Studio Grabber program. Dye penetration depth at the restoration/dentin and the restoration/enamel interfaces was measured as a rate of the entire depth of the restoration using a software (Scion image, Scion Corp., U.S.A.) The data were analysed statistically using One-way ANOVA and Tukey's method. The results were as follows : 1. Pulse-Delay group did not show any significant difference in dye penetration rate from other groups at enamel and dentin margins (p>0.05) 2. At dentin margin, ultra-high intensity group showed significantly higher dye penetration rate than both regular intensity group and low intensity group (p<0.05). 3. At enamel margin, there were no statistically significant difference among four groups (p>0.05). 4. Dentin margin showed significantly higher dye penetration rate than enamel margin in all groups (p<0.05).

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.151-165
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• 2010

Images are often corrupted by impulse noise due to a noise sensor or channel transmission errors. The filter based on SVM(Support Vector Machine) and the improved adaptive median filtering is proposed to preserve image details while suppressing impulse noise for image restoration. Our approach uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a noisy pixel, the improved adaptive median filter is used to replace it. To demonstrate the performance of the proposed filter, extensive simulation experiments have been conducted under both salt-and-pepper and random-valued impulse noise models to compare our method with many other well known filters in the qualitative measure and quantitative measures such as PSNR and MAE. Experimental results indicate that the proposed filter performs significantly better than many other existing filters.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.69-77
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• 2008

An algorithm for tracking the trace of structured light is proposed to obtain depth information accurately. The technique is based on the fact that the pixel location of light in an image has a unique association with the object depth. However, sometimes the projected light is dim or invisible due to the absorption and reflection on the surface of the object. A dynamic programming approach is proposed to solve such a problem. In this paper, necessary mathematics for implementing the algorithm is presented and the projected laser light is tracked utilizing a dynamic programming technique. Advantage is that the trace remains integrity while many parts of the laser beam are dim or invisible. Experimental results as well as the 3-D restoration are reported.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.423-432
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• 2018

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2029-2036
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• 2017

Currently, the demand for multimedia services is increasing with the rapid development of the digital age. Image data is corrupted by various noises and typical noise is mainly AWGN, salt and pepper noise and the complex noise that these two noises are mixed. Therefore, in this paper, the noise is processed by classifying AWGN and salt and pepper noise through noise judgment. In the case of AWGN, the outputs of spatial weighted filter and pixel change weighted filter are composed and processed, and the composite weights are applied differently according to the standard deviation of the local mask. In the case of salt and pepper noise, cubic spline interpolation and local histogram weighted filters are composed and processed. This study suggested the multiple image restoration filter algorithm which is processed by applying different composite weights according to the salt and pepper noise density of the local mask.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.