• Journal of IKEEE
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• v.22 no.4
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• pp.1242-1245
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• 2018

In this paper, we have implemented a network that overcomes the over-blurring phenomenon that occurs when removing Gaussian noise. In the conventional filtering method, blurring of the original image is performed to remove noise, thereby eliminating high frequency components such as edges and corners. We propose a network that reducing over blurring while maintaining denoising performance by adding denoised high frequency components to denoisers based on CNN.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1707-1710
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• 2003

In this paper, we propose a method of decision on blurring for business card images using block classification. In the proposed method, an input image is partitioned into 8${\times}$8 blocks and each block is classified into character block or background block using a block energy calculated in DCT domain. Whether the input image is blurring or non-blurring is determined using a ratio of low frequency energy and high frequency energy in DCT domain. Experimental results show that the proposed block classification classifies block well and the proposed decision on blurring decides well for various business card images.

• Journal of the Optical Society of Korea
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• v.18 no.5
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• pp.472-476
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• 2014

The Joint Transform Correlator (JTC) has been the most suitable technique for real time optical pattern recognition and target tracking applications. This paper proposes a new application of the JTC system for an analysis of the blurring effect of the optical images caused by a defocused lens. We present the relation between the correlation peak, optical transfer function (OTF), and the amount of blurring caused by focusing error. Moreover, we show a possibility of calibrating the blurred image by simply measuring the correlation peak.

• Journal of IKEEE
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• v.21 no.1
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• pp.46-65
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• 2017

This paper proposes a fast patch-based de-blurring algorithm including kernel estimation based on the angle between the edge and the blur direction. For de-blurring, image patches from the most informative edges in the blurry image are used to estimate a kernel with low computational cost. Moreover, the kernels of each patch are estimated based on the correlation between the edge direction and the blur direction. This makes the final kernel more reliable and creates an accurate latent image from the blurry image. The combination of directionally oriented kernel estimation and patch-based de-blurring is faster and more accurate than existing state-of-the art methods. Experimental results using various test images show that the proposed method achieves its objectives: speed and accuracy.

• Korean Journal of Digital Imaging in Medicine
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• v.15 no.2
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• pp.55-61
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• 2013

The purpose of this study is to evaluate image blurring according to variation of the ETL and propose the clinically appropriate ETL range. SIEMENS MAGNETOM Skyra 3.0T and 20 channel head coil were used for the study. MRI phantom was kept the lines horizontally to three direction(X,Y,Z) of the coil and T1, T2 weighted images that used the fast spin echo technique acquired. The ETL with increase of 10 was applied from 10 to 80. In addition, the ETL with increase of 1 was applied in the interval statistically significant differences occurred. And T1, T2 weighted images that used the conventional spin echo technique acquired to compare image blurring of the images that used the fast spin echo technique. The slope of lattice in the images was measured using Image J 1.47v program to evaluate image blurring. And image blurring was determined by the degree of the slope. The statistical significance of both techniques was evaluated by the Kruskal-Wallis test of the SPSS 17.0v. And the correlation of the ETL and image blurring was evaluated quantitatively by regression analysis. The slope of the T1, T2 weighted images that used fast spin echo technique decreased as contrasted with conventional spin echo technique. In the result of the Kruskal-Wallis test, the T1, T2 weighted images that used fast spin echo technique made a significant difference with conventional spin echo technique. Particularly, in the Tomhane' T2 test, the T1, T2 weighted images made a significant difference from ETL 22 and 31 respectively. In the result of the regression analysis, the R-squared of the T1, T2 weighted images are 0.762 and 0.793. It is difficult to apply the long ETL in the T1 weighted image caused by the short TR and multi-slices study. Therefore, clinical impact according to variation of the ETL is very slight in the T1 weighted images. But the application of the proper ETL is demanded in T2 weighted images using the fast spin echo technique in order to prevent image blurring.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.6
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• pp.75-80
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• 1983

When an image with spot noise is smoothed by smoothing filters, the noise is almost eliminated However, the image is blurred. The algorithm that reduces such an image blurring is proposed in this paper. In the algorithm, the difference between noisy image and median filtered noisy image is smoothed. As the re-smoothing method, the absolute value of the difference is median filtered and the sign of the difference is affixed on the result. It is shown that the proposed algorithm is quite effective for noise elimination and also for image blurring decrease at the same time. In this paper, the algorithm is compared with the other smoothing methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4C
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• pp.408-414
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• 2010

When we are collecting traffic information on CCTV images, we have to install the detect zone in the image area during pan-tilt system is on duty. An automation of detect zone with pan-tilt system is not easy because of machine error. So the fisheye lens attached camera or convex mirror camera is needed for getting wide area images. In this situation some troubles are happened, that is a decreased system speed or image distortion. This distortion is caused by occlusion of angled ray as like trembled snapshot in digital camera. In this paper, we propose two methods of de-blurring to overcome distortion, the one is image segmentation by nonlinear diffusion equation and the other is deformation for some segmented area. As the results of doing de-blurring methods, the de-blurring image has 15 decibel increased PSNR and the detection rate of collecting traffic information is more than 5% increasing than in distorted images.

• Journal of Biosystems Engineering
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• v.17 no.4
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• pp.363-369
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• 1992

The aim of this study was to examine the blurring effects on performance of the experimental fruit grader in grading Fuji apples by size and coloration of the whole surface of individual apples. The grader consisted of a black/white image prcessing system, one camera, and utilized the algorithm developed for high speed sorting in the previous study. The results are summarized as follows : 1. With the algorithm developed in the previous study, it took 0.27~0.33 second in analyzing the size and coloration of an apple, and relative errors were within 3% for size and 1.3% for coloration. 2. The effect of blurring increased linearly with the conveying speed of apple and showed more significant effect on detection of coloration than on determining of size. 3. Considering the blurring effect, capacity of the experimental fruit grader was estimated to 7,500 apples per hour.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.39-42
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• 2023

Recently, with the explosive increase of video streaming services, real-time live broadcasting has also increased, which leads to an infringement problem for user privacy. So, to solve such problems, we proposed the real image blurring system using dlib face-recognition library. 68 face landmarks are extracted and convert into 128 vector values. After that the proposed system tries to compare this value with the image in the database, and if it is over 0.45, it is considered as different person and image blurring processing is performed. With the proposed system, it is possible to solve the problem of user privacy infringement, and also to be utilized to detect the specific person. Through experimental results, the proposed system has an accuracy of more than 90% in terms of face recognition.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.4
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• pp.562-569
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• 2010

This paper analyzes the problems that occurred in the magnification process for a fine input image and investigates a method to improve the blurring of magnified image. This paper applies a curve interpolation algorithm in CAD/CAM for the same test images with the existing image algorithm in order to improve the blurring of magnified image. As a result, the nearest neighbor interpolation, which is the most frequently applied algorithm for the existing image interpolation algorithm, shows that the identification of a magnified image is not possible. Therefore, this study examines an interpolation of gray-level data by applying a low-pass spatial filter and verifies that a bilinear interpolation presents a lack of property that accentuates the boundary of the image where the image is largely changed. The periodic B-spline interpolation algorithm used for curve interpolation in CAD/CAM can remove the blurring but shows a problem of obscuration, and the Ferguson' curve interpolation algorithm shows a more sharpened image than that of the periodic B-spline algorithm. For the future study, hereafter, this study will develop an interpolation algorithm that has an excellent improvement for the boundary of the image and continuous and flexible property by using the NURBS, Ferguson' complex surface, and Bezier surface used in CAD/CAM engineering based on the results of this study.