• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1865-1872
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• 1997

This paper proposes not only the thresholding problem which has been one of the major problems in the pre-existing edge detection method but also the removal of blurring effect occurred at the edge regions due to the smoothing process. The structure of a given image is assigned as one of the three predefined image structure classes by evaluating its toll membership value to each reference structure class:The structure of an image belongs to the structure class which has the least cost value with the image. Upon the structure class assigned, noise removal and edge extraction precesses are performed, e.g., the smoothing algorithm is applied to the image if its structure belongs to the pure noise region class; edge extraction while removing the noise is performed simultaneously if the edge structure class. The proposed method shows that preventing the blurring effect can be usually seen in the edge images and extracting the edges with no using thresholding value by the experiments.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.104-107
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• 2007

인간은 초점정보를 이용하여 단안만으로도 공간의 깊이를 지각할 수 있다. 이것은 한 번에 하나의 대상물에만 초점을 맞출 수 있고 그 외의 부분은 흐림 현상을 유도함으로써 이루어진다. 이는 초점이 맞는 대상물체로부터 멀어지면 멀어질수록 흐림 현상이 강해지는 원리를 이용한 것으로 주파수 성분의 변화량에 대한 연산과 깊은 관련이 있다. 본 논문에서는 이와 같은 인간의 시각 시스템의 요소 중 하나인 초점정보를 모방하여 초점거리가 다른 각각의 이미지들에 각각의 가중치를 부여하였다. 그리고 각 이미지들을 일정 블록으로 각각 분할하여 초점이 가장 잘 맞는 블록을 찾아내어 하나의 이미지로 통합하였다. 이때 각 영역은 자신이 속했던 이미지의 가중치를 따르게 한다. 각 이미지에서 가장 포커스 수치가 높은 영역을 찾기 위한 방법으로 주파수 영역 기반 처리와 공간 영역 기반 처리를 결합 하였다. 주파수 기반으로는 FFT(Fast Fourier Transform)에서 고주파 부분의 영역을 뽑아내어 포커스수치를 계산하였으며, 공간 영역 처리 기반으로는 이웃픽셀과의 차이가 임계값이하인 것을 제외한 영역을 뽑아내어 저주파 영역의 연산을 제거하는 방법과 단순히 Laplacian measure만을 사용하여 저주파까지도 포함한 방법의 두 가지를 적용하였다. 최종적으로 3개의 포커스 측정값을 결합시켜 포커스 수치를 계산한 후 각 블록의 가중치에 맞게 하나의 이미지로 통합하여 상대적 깊이지도를 생성하였다.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.3-12
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• 2022

Image deblurring aims to remove image blur, which can be generated while shooting the pictures by the movement of objects, camera shake, blurring of focus, and so forth. With the rise in popularity of smartphones, it is common to carry portable digital cameras daily, so image deblurring techniques have become more significant recently. Originally, image deblurring techniques have been studied using traditional optimization techniques. Then with the recent attention on deep learning, deblurring methods based on convolutional neural networks have been actively proposed. However, most of them have been developed while focusing on better performance. Therefore, it is not easy to use in real situations due to the speed of their algorithms. To tackle this problem, we propose a novel deep learning-based deblurring algorithm that can be operated in real-time on HD resolution. In addition, we improved the training and inference process and could increase the performance of our model without any significant effect on the speed and the speed without any significant effect on the performance. As a result, our algorithm achieves real-time performance by processing 33.74 frames per second at 1280×720 resolution. Furthermore, it shows excellent performance compared to its speed with a PSNR of 29.78 and SSIM of 0.9287 with the GoPro dataset.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.105-111
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• 2009

In this paper, we propose a locally adaptive image restoration method based on nonlinear extrapolation in frequency domain. In general, the conventional method causes ringing artifacts on the object boundary. To solve this problem, we introduce an improved restoration method which considers textures of an image block. In the proposed method, a blurred image is divided into several blocks, and each block is classified into three groups; simple, one edge, and complex blocks according to the contained texture. Depending on the classification result, adaptive nonlinear extrapolation is applied to each block in a blurred image. Experimental results show that the proposed algorithm can achieve higher quality image in both subjective and objective views as compared with the conventional method.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1286-1301
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• 2003

This paper introduces segmentation based environment modeling method and integration method using multiple environment map for constructing the realtime image-based panoramic navigation system. The segmentation-based environment modeling method is easy to implement on the environment map and can be used for environment modeling by extracting the depth value by the segmentation of the environment map. However, an environment model that is constructed using a single environment map has the problem of a blurring effect caused by the fixed resolution, and the stretching effect of the 3D model caused when information that does not exist on the environment map occurs due to the occlusion. In this paper, we suggest environment models integration method using multiple environment map to resolve the above problem. This method can express parallax effect and expand the environment model to express wide range of environment. The segmentation-based environment modeling method using multiple environment map can build a detail model with optimal resolution.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.43-48
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• 2014

Usually, image deconvolution is applied as a preprocessing step in surveillance systems to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose a blind-image deconvolution filtering approach based on genetic programming (GP). A numerical expression is developed using GP process for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded image. The performance of developed function is estimated using various degraded image sequences. Our comparative analysis highlights the effectiveness of the proposed filter.

• Progress in Medical Physics
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• v.27 no.1
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• pp.1-7
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• 2016

In this work, we considered a compressed-sensing (CS)-based image deblurring scheme with a total-variation (TV) regularization penalty for improving image characteristics in digital tomosynthesis (DTS). We implemented the proposed image deblurring algorithm and performed a systematic simulation to demonstrate its viability. We also performed an experiment by using a table-top setup which consists of an x-ray tube operated at $90kV_p$, 6 mAs and a CMOS-type flat-panel detector having a $198-{\mu}m$ pixel resolution. In the both simulation and experiment, 51 projection images were taken with a tomographic angle range of ${\theta}=60^{\circ}$ and an angle step of ${\Delta}{\theta}=1.2^{\circ}$ and then deblurred by using the proposed deblurring algorithm before performing the common filtered-backprojection (FBP)-based DTS reconstruction. According to our results, the image sharpness of the recovered x-ray images and the reconstructed DTS images were significantly improved and the cross-plane spatial resolution in DTS was also improved by a factor of about 1.4. Thus the proposed deblurring scheme appears to be effective for the blurring problems in both conventional radiography and DTS and is applicable to improve the present image characteristics.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.763-772
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• 2020

During X-ray examinations in the DR system, the scattered X-rays physically generated by the patient cause image blurring in poor quality. Although X-rays to increase the contrast of images, this increases the patient's exposure dose and is likely to result in grid induced artifacts. Therefore, the purpose of this study is obtain images similar to those of real-grid with non-grid level conditions using a VGR (Virtual Grid Role) algorithm that serves as a virtual grid. Comparing MTF, SNR and CNR of non-grid and VGR algorithm images obtained with 70% exposure conditions of real-grid images showed that the MTF0.5 differed from 0.265 to 0.350 and the MTF0.1 from 0.412 to 0.467 and the SNR, CNR were also different. In addition, comparing MTF, SNR and CNR of VGR algorithm and real-grid images showed that the MTF0.5 differed from 0.350 to 0.367 and the MTF0.1 from 0.467 to 0.483 and the SNR, CNR by little.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.225-236
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• 2013

3D contents could cause unique 3D visual fatigue. Screen disparity, image blurring, and crosstalk are known to be the three major factors responsible for the fatigue. Among these, screen disparity and image blurring are content factors, that is, one can directly manipulate contents themselves to handle visual fatigue caused by these two factors. On the other hand, because crosstalk is closely tied to physical characteristics of 3D display, it is difficult or even impossible to reduce crosstalk-driven visual fatigue unless one replaces 3D display itself (for example, from active to passive display). However, the effects of crosstalk on 3D visual fatigue depends on visual stimulus features (that is, contents), and thus it is possible to manipulate stimulus features in order to handle visual fatigue caused by crosstalk. Hence, this research tested the effects of visual stimulus features on crosstalk (which then causes 3D visual fatigue). Using relative depth discrimination task, we tested the effects of stimulus-background contrast, background texture density, and screen disparity on the degree of perceived crosstalk. The results showed that crosstalk decreases with presence of background texture and with less degree of screen disparity.

• Journal of Korean Ophthalmic Optics Society
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• v.21 no.3
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• pp.259-264
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• 2016

Purpose: To establish the cause of decrease in body stability and to analyze the effects on sensory organs maintaining static balance according to the induced astigmatic blur. Methods: Twenty subjects (10 males, 10 females; mean age, $23.40{\pm}2.70years$) were participated in this study. To induce myopic simple astigmatism, the axis directions of cylindrical lenses were placed $180^{\circ}$ on both eyes (with-the-rule), $90^{\circ}$ on both eyes (against-the-rule), and $45^{\circ}$ on both eyes (oblique). Cylindrical lenses of +0.50, +1.00, +1.50, +2.00, +3.00, +4.00, and +5.00 D were used to increase astigmatic blur in each astigmatism types. General stability (ST) and sway power (SP) in frequencies by each sensory organs were analyzed using the TETRAX biofeedback system. Results: ST in the all astigmatism types were raised with increase of astigmatic blur compared to full corrected condition, but a significant difference only showed in the induced oblique astigmatism. According to the results of correlation analysis between ST and SP in the each frequencies with increase of astigmatic blur, the causes of increased ST in the induced oblique astigmatism showed to have a high correlation in order of somatosensory system (high-medium frequency), central nervous system (high frequency), peripheral vestibular system (low-medium frequency), and visual system (low frequency). Conclusions: The visual information by uncorrected oblique astigmatism may disturb the normal functions of all sensory organs maintaining body balance, consequently, the body stability can be reduced. Therefore, optimal correction of astigmatism can play an important role for reducing the instability of body balance.