• Journal of Korea Multimedia Society
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• v.16 no.10
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• pp.1180-1188
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• 2013

Interpolation or super-resolution is used in order to restore degradation of image quality that appears after various transform of image. The method on subjective or objective image resolution improvement having low computation complexity has been being researched in many different ways. In this paper, image enhancement method using improved self degradation restoration(ISDR) method is proposed. The proposed method uses ISDR to estimate pixel value of missed coordinate in the process of image scaling, and combines the estimated loss information and interpolated image to generate enhanced result image. The proposed method shows that PSNR increases by 1.8dB, and subjective image quality is superior to other compared methods. The proposed method can be applied as a basis technique in variety of applications which requires image scale transform.

• Journal of Biomedical Engineering Research
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• v.21 no.3 s.61
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• pp.233-237
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• 2000

In general. fiber-optic medical endoscopes are made from glass step index (SI) fibers. These endoscopes have limitations in both image quality and mechanical properties. In particular. the image resolution of the SI endoscopes is limited to about 5$\mu$m. In this study the image resolution of plastic graded index (GRIN) super-resolution image guides with pixel sizes from 7 to 2.5 $\mu$m were measured and compared with those of 91ass SI image guides. There is an improvement in resolution of the plastic GRIN image guides as the microfiber diameter is reduced from 7 $\mu$m to 2.5 $\mu$m. The measured resolution of plastic GRIN image guide with 2.5 $\mu$m microfibers is more than a factor of two higher than that of g1ass SI image guide with 5$\mu$m microfibers. This new design of optical systems could have a major impact on a wide array of future optical systems used in defense. industrial, and medical applications.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.149-156
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• 2011

This paper proposes the effective edge selection algorithm for the rapid processing time and low memory usage of efficient graph-based image segmentation on mobile device. The graph-based image segmentation algorithm is to extract objects from a single image. The objects are consisting of graph edges, which are created by information of each image's pixel. The edge of graph is created by the difference of color intensity between the pixel and neighborhood pixels. The object regions are found by connecting the edges, based on color intensity and threshold value. Therefore, the number of edges decides on the processing time and amount of memory usage of graph-based image segmentation. Comparing to personal computer, the mobile device has many limitations such as processor speed and amount of memory. Additionally, the response time of application is an issue of mobile device programming. The image processing on mobile device should offer the reasonable response time, so that, the image segmentation processing on mobile should provide with the rapid processing time and low memory usage. In this paper, we demonstrate the performance of the effective edge selection algorithm, which effectively controls the edges of graph for the rapid processing time and low memory usage of graph-based image segmentation on mobile device.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.733-735
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• 2014

Example based Super Resolution(SR) is using the correspondence between the low and high resolution image from a database. This method uses only one image to estimate a high resolution image and can get the larger image than 2 times. Example based SR is proposed to solve the problem of classical SR. Neighbor embedding(NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the poor generalization of NE decreases the performance of such algorithm. The sizes of local training sets are always too small to improve the performance of NE. We propose the advanced NE baesd on SVR having an excellent generalization ability to solve this problem. Given a low resolution image, we estimate a pixel in its high resolution version by using SVR based NE. Through experimental results, we quantitatively and qualitatively confirm the improved results of the proposed algorithm when comparing with conventional interpolation methods and NE.

• Proceedings of the KSRS Conference
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• v.2
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• pp.804-807
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• 2006

The SRI(Super Resolution Imager) with 800mm aperture primary mirror is the ground development model of the high resolution satellite camera. The SRI focal plane electronics including detector array generates the data for high-resolution images by converting incoming light into digital stream of pixel data. Since the focal plane including a detector is the basic building block of the camera system, the main system performances is directly determined by its performance. This paper measures the SRI focal plane electronics’ performance such as the dark signal, the dark signal noise, the linearity, the PRNU(Photo Response Non-Uniformity), the SNR(Signal to Noise Ratio) and the sensor saturation capability. In addition, this paper verifies the various functionalities of the SRI focal plane electronics. The electrical test equipment with the specialized software and the optical test equipments such as the integrating sphere, the rotation stage and the target are implemented and used to verify these functionalities and performances.

• Journal of IKEEE
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• v.12 no.2
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• pp.124-130
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• 2008

Vector Graphics describes an image with mathematical statements instead of pixel information. Which enables easy scalability without loss in image quality and usually results in a much smaller file size compared with bitmap images. In this paper, we propose Vector Graphics Accelerator for mobile device with scan-line edge flag algorithm to render vector image without sorting process of edge. Proposed Vector Graphics Accelerator was verified with OpenVG 2D Vector image. The estimated processing time of proposed Accelerator with Tiger image is 12ms on Tessellation process, and total rendering time is 208ms. Estimated rendering performance with Tiger image is about 5 frame per second

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.223-229
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• 2018

This paper proposes a robust technique of image segmentation, which can be obtained if the topological persistence of each connected component is used as the feature vector for the graph-based image segmentation. The topological persistence of the components, which are obtained from the super-level set of the image, is computed from the morse function which is associated with the gray-level or color value of each pixel of the image. The procedure for the components to be born and be merged with the other components is presented in terms of zero-dimensional homology group. Extensive experiments are conducted with a variety of images to show the more correct image segmentation can be obtained by merging the components of small persistence into the adjacent components of large persistence.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8C
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• pp.824-831
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• 2005

In the reconstruction of high resolution (HR) images from low resolution (LR) images frames, the error in the estimated motion degrades the reliability of the reconstructed HR image. Some methods were recently proposed where motion estimation and HR reconstruction is performed simultaneously. The estimated motion is still prone to error when it is based on a simple block matching. In this paper, we propose a reconstruction of a HR image by applying POCS and a regularized block matching simultaneously. In this method, a motion vector is obtained from a regularized block matching algorithm since the motion of a pixel in an image is highly correlated with the motion in neighboring regions. Experimental results show that the improved accuracy of the estimated motion vectors results in higher PSNR of the reconstructed HR images.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1254-1260
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• 2000

There have been many developments in super precision working technique and working method up to, now. But, it is very difficult to evaluate working surface accurately without the technicians experience and judgment. Surface roughness tester using stylus was used to measure surface condition generally But this method is not so desirable because of damage on test piece caused by contact between the workpiece and the stylus sensor. As a result, non-contact method was known as a good way to carry, out this process without damage. However, this is a difficult one among the various measuring methods. So we are tying to suggest a new method using texture analysis through image processing to get a surface information in worked test piece. Co-occurrence matrix using difference of gray levels between a pixel and its neighboring one was used to study behavior of surface roughness and to J acquire data for analysis. Standard specimen was adapted to verify this research. We suggest texture information method in order to evaluate surface state for the best measurement system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3180-3199
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• 2013

While non-predefined object segmentation (NDOS) distinguishes an arbitrary self-assumed object from its background, predefined object segmentation (DOS) pre-specifies the target object. In this paper, a new and novel method to segment predefined objects is presented, by globally optimizing an orientation-based objective function that measures the fitness of the object boundary, in a discretized parameter space. A specific object is explicitly described by normalized discrete sets of boundary points and corresponding normal vectors with respect to its plane shape. The orientation factor provides robust distinctness for target objects. By considering the order of transformation elements, and their dependency on the derived over-segmentation outcome, the domain of translations and scales is efficiently discretized. A branch and bound algorithm is used to determine the transformation parameters of a shape model corresponding to a target object in an image. The results tested on the PASCAL dataset show a considerable achievement in solving complex backgrounds and unclear boundary images.