• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.1-11
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• 2006

This paper presents a digital auto-focusing method using a priori estimated point-spread-functions (PSF) database. The proposed algorithm efficiently removes out-of-focus blur in a degraded input image by selecting the optimal PSF from the database. The database consists of optical characteristics of image formation system. The PSF selection Process is performed based on a novel focusing measure. The proposed method includes a spatially adaptive filter for removing both noise and ringing artifacts. Experimental results show that the proposed method efficiently removes out-of-focus blur using significantly reduced computational load compared with the existing method.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.31-43
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• 2014

A depth map is an important component for stereoscopic image generation. Since the depth map acquired from a depth camera has a low resolution, upsamling a low-resolution depth map to a high-resolution one has been studied past decades. Upsampling methods are evaluated by objective evaluation tools such as PSNR, Sharpness Degree, Blur Metric. As well, the subjective quality is compared using virtual views generated by DIBR (depth image based rendering). However, works on the analysis of the relation between depth map upsampling and stereoscopic images are relatively few. In this paper, we investigate the relationship between subjective evaluation of stereoscopic images and objective performance of upsampling methods using cross correlation and linear regression. Experimental results demonstrate that the correlation of edge PSNR and visual fatigue is the highest and the blur metric has lowest correlation. Further, from the linear regression, we found relative weights of objective measurements. Further we introduce a formulae that can estimate 3D performance of conventional or new upsampling methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.88-95
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• 1996

Recently, the use of color data is growing fast in the area of image processing. To represent full resolution image on a limited output device, image has to be quantized an dithered. So, many dithering techniques are foundd in the printing. In this paper, we propose nonlinear quantization to consider the overlapping phenomena of neighboring printing dots and modified dot diffusion algorithm to compensate the color degradation produced in the quantization process. In the modified dot-diffusion quantization errors to be diffused are adjusted to improve both image blur and color change produced in the dot diffusion. The printed image obtained by the proposed color dithering method has higher visual quality an less color degradation than the images by conventional printing method.

• 한국정보디스플레이학회:학술대회논문집
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• 2003.07a
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• pp.694-697
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• 2003

In applying LCD to TV application, one of the most significant factors to be improved is image sticking on the moving picture. LCD is different from CRT in the sense that it's continuous passive device, which holds images in entire frame period, while impulse type device generate image in very short time. To reduce image sticking problem related to hold type display mode, we made an experiment to drive LCD like CRT. We made articulate images by turn on-off backlight, and we realized the ratio of Back Light on-off time by adjusting between on time and off time for video signal input during 1 frame (16.7ms). Conventional CCFL (cold cathode fluorescent lamp) cannot follow fast on-off speed, so we evaluated new fluorescent substances of light source to improve residual light characteristic of CCFL. We realized articulate image generation similar to CRT by blinking drive. As a result, reduced image sticking phenomenon was validated by naked eye and response time measurement.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.107-112
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• 1997

This paper proposes the regularized constrained iterative image restoration algorithms which apply new space-adaptive methods to degraded image signals, and analyzes the convergence condition of the proposed algorithm. First, we introduce space-adaptive regularization operators which change according to edge characteristics of local images in order to effectively prevent the restored edges and boundaries from reblurring. And, pseudo projection operator is used to reduce the ringing artifact which results from extensive amplification of noise components in the restoration process. The analysed algorithm is stable convergent to the fixed point. According to the experimental results for various signal-to-noise ratios(SNR) and blur models, the proposed algorithms other methods and is robust to noise effects and edge reblurring by regularization especially.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12C
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• pp.1010-1019
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• 2010

This article presents a PDE-based interpolation algorithm to effectively reproduce high resolution imagery. Conventional PDE-based interpolation methods can produce sharp edges without checkerboard effects; however, they are not interpolators but approximators and tend to weaken fine structures. In order to overcome the drawback, a texture enhancement method is suggested as a post-process of PDE-based interpolation methods. The new method rectifies the image by simply incorporating the bilinear interpolation of the weakened texture components and therefore makes the resulting algorithm an interpolator. It has been numerically verified that the new algorithm, called the PDE-based image interpolator (PII), restores sharp edges and enhances texture components satisfactorily. PII outperforms the PDE-based skeleton-texture decomposition (STD) approach. Various numerical examples are shown to verify the claim.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.309-323
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• 1997

Although there are many photochemical images of which are censored while they are recorded, normal approaches are often applied to the restorations for them. In this case, it yields a restored image which might have serious bias. However, solutions for this problem are hardly found in the research of image restorations. This article provides a method of image restoration via EM algorithm for the censored images of which are contaminated with Gaussian noise and blur, also presents some results of simulation for artificial images censorized.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.48-58
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• 2022

Blind restoration for motion-blurred images is always the research hotspot, and the key for the blind restoration is the accurate blur kernel (BK) estimation. Therefore, to achieve high-quality blind image restoration, this thesis presents a novel windowed-total-variation method. The proposed method is based on the spatial scale of edges but not amplitude, and the proposed method thus can extract useful image edges for accurate BK estimation, and then recover high-quality clear images. A large number of experiments prove the superiority.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.71-77
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• 2011

In this paper, we propose a learning based super resolution algorithm using local block for image enhancement of vehicle license plate. Local block is defined as the minimum measure of block size containing the associative information in the image. Proposed method essentially generates appropriate local block sets suitable for various imaging conditions. In particular, local block training set is first constructed as ordered pair between high resolution local block and low resolution local block. We then generate low resolution local block training set of various size and blur conditions for matching to all possible blur condition of vehicle license plates. Finally, we perform association and merging of information to reconstruct into enhanced form of image from training local block sets. Representative experiments demonstrate the effectiveness of the proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2121-2128
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• 2015

In this paper presents a method of extracting reliable and regions of interest (ROI) in barcode for the purpose of factory automation. backgrounds are separated based on directional components and the characteristics of detected patterns. post-processing is performed on candidate images with analysis of problems caused by blur, rotation and areas of high similarity. In addition, the resizing factor is used to achieve faster calculations through image resizing. The input images contained multiple product or barcode for application to diverse automation environments; a high extraction success rate is accomplished despite the maximum shooting distance of 80 cm. Simulations involving images with various shooting distances gave an ROI detection rate of 100% and a post-processing success rate of 99.3%.