• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.387-394
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• 2022

120 kVp FBP reconstruction image standard by using raw data after scanning by changing tube voltage among the NECK CT protocols that are broad applied in clinical practice using a human phantom including thyroid gland The usefulness of the DLIR reconstruction technique was investigated. As a result, CTDIvol decreased when the DLIR reconstruction technique was applied, and in particular, the image quality obtained under the same standard scanning conditions at a lower dose for ASIR-V and DLIR reconstruction was reached than when FBP was applied at the same kVp In addition, as a result of SNR and CNR analysis, the DLIR reconstructed image was analyzed with high SNR and CNR values, and SSIM analysis, the SSIM index of the 100 kVp, DLIR reconstructed image was measured to be close to 1, and it was analyzed that the similarity of the reconstructed image to the original image was high (p>0.05). If the results of this study are used to supplement clinical image evaluation and further develop an algorithm applicable to various anatomical structures, it is thought that it will be useful for clinical application as it is possible to maintain the image quality while lowering the examination dose.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1994.10a
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• pp.95-102
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• 1994

This paper presents a Cable Stayed Bridge Construction Management System, which consists of Structural System Identification Method (SSIM), Error Sensitivity Analysis and Optimum Error Adjustment & Prediction System. The 1st System Identification Method builds an error influence matrix using the linear superposition of each error modes. The 2nd SSIM also considers the second error mode term, which shows good error factor estimation. The optimal cable adjustment can be accomplished within the allowable range of both cable tension and camber. The Post processor, constituted with Motif and GL library on SGI platform, is useful for monitoring construction stage management by displaying construction data, adjustment and prediction results at each construction step.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.60-64
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• 2011

In this paper, we propose a new post-processing technique that removes blocking and ringing artifacts in Block discrete cosine transformation (BDCT)-coded images using bilateral filtering. The selection of filter parameters is a key issue in the application of a bilateral filter because it significantly affects the result. An efficient method of selecting the bilateral filter parameters is presented. The experimental results show that the proposed approach alleviates the artifacts efficiently in terms of PSNR, MSDS, and SSIM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.542-543
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• 2018

In this paper, we propose a method of visual information of original image by encrypting only partial data of infra-red image. In this method, the infra-red image is divided into R, G, and B channels, and the outline is searched for the three channel images. Then, an average value is calculated for each contour image, and a channel to be encrypted is selected. The wavelet transform is performed on the selected channel to encrypt only the LL subband in which the highest energy is concentrated. Experimental results show that the SSIM value is less than 0.09 in the case of the proposed encryption method.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.837-840
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• 2015

SSIM은 인간의 시각 체계가 이미지의 구조적 정보에 예민하다는 점을 이용하여 여러 가지 구조적 정보들의 유사성을 계산함으로써 이미지를 평가하는 대표적인 이미지 평가 기법이다. 하지만 SSIM은 컬러 이미지들에 대해 색 차이를 고려하지 못하는 문제가 있다. 이러한 문제를 해결하기 위해, HSI 색 공간을 활용한 SHSIM 기법이 제안되었으나 이 기법 또한 두 컬러 이미지 간 인지적인 색 차이를 충분히 반영하지는 못하고 있다. 본 논문에서는 CIE Lab 색 공간을 도입하여 대응 되는 픽셀들의 인지적 색 차이를 계산하여 이미지 평가에 활용하는 방법을 제안한다. 제안하는 기법의 성능을 평가하기 위해, 이미지 평가 분야에서 가장 많이 알려진 네 가지의 데이터베이스와 네 종류의 평가 기준들을 이용하였다. 실험 결과에서는 제안하는 기법이 다른 기법들보다 인간 시각 체계와 더 상관성이 높다는 것을 보여줌으로써 성능을 증명하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.827-833
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• 2012

In this paper, we propose an improved optical flow algorithm which reduces computational complexity as well as noise level. This algorithm reduces computational time by applying level simplification technique and removes noise by using eigenvectors of objects. Optical flow is one of the accurate algorithms used to generate depth information from two image frames using the vectors which track the motions of pixels. This technique, however, has disadvantage of taking very long computational time because of the pixel-based calculation and can cause some noise problems. The level simplifying technique is applied to reduce the computational time, and the noise is removed by applying optical flow only to the area of having eigenvector, then using the edge image to generate the depth information of background area. Three-dimensional images were created from two-dimensional images using the proposed method which generates the depth information first and then converts into three-dimensional image using the depth information and DIBR(Depth Image Based Rendering) technique. The error rate was obtained using the SSIM(Structural SIMilarity index).

• Fashion & Textile Research Journal
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• v.15 no.6
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• pp.1010-1017
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• 2013

Current global textiles and fashion industries have gradually shifted focus to high value-added, high sensibility, and multi-functional products based on new human-friendliness and sustainable growth technologies. Textile design CAD systems have been developed in conjunction with computer hardware and software sector advances. This study compares the patterns or images of actual woven fabrics and virtual fabrics prepared with a textile design CAD system. In this study, several weave structures (such as fancy yarn weave and patterns) were prepared with a shuttle loom. The woven textile images were taken using a CCD camera. The same weave structure data and yarn data were fed into a textile design CAD system in order to simulate fabric images as similarly as possible. Similarity Index analysis methods allowed for an analysis of the index between the actual fabric specimen and the simulated image of the corresponding fabric. The results showed that repeated small pattern weaves provide superior similarity index values than those of a fancy yarn weave that indicate some irregularities due to fancy yarn attributes. A Complex Wavelet Structural Similarity(CW-SSIM) index resulted in a better index than other methods such as Multi-Scale(MS) SSIM, and Feature Similarity(FS) SSIM, across fabric specimen images. A correlation analysis of the similarity index based on an image analysis and a similarity evaluation by panel members was also implemented.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.19-26
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• 2020

In this paper, we propose a method to analyze the loss region of the dictionary-based super resolution result learned for image quality improvement and to map the learning data according to the analyzed loss region. In the conventional learned dictionary-based method, a result different from the feature configuration of the input image may be generated according to the learning image, and an unintended artifact may occur. The proposed method estimate loss information of low resolution images by analyzing the reconstructed contents to reduce inconsistent feature composition and unintended artifacts in the example-based super resolution process. By mapping the training data according to the final interpolation feature map, which improves the noise and pixel imbalance of the estimated loss information using a Gaussian-based kernel, it generates super resolution with improved noise, artifacts, and staircase compared to the existing super resolution. For the evaluation, the results of the existing super resolution generation algorithms and the proposed method are compared with the high-definition image, which is 4% better in the PSNR (Peak Signal to Noise Ratio) and 3% in the SSIM (Structural SIMilarity Index).

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.177-183
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• 2020

Steganography uses digital images as a medium for sending secret messages over insecure networks. There is also a least significant bit(LSB) that is a popular method of embedding secret messages in digital images. The goal of steganography is to securely and flawlessly transmit secret messages using stego media over a communication channel. There is a need for a method to improve resistance to reduce the risk of exposure to third parties. To safely hide secret messages, I propose new algorithms that go through crossing, encryption, chaos and concealment steps. After separating Hangul syllables into choseong, jungseong and jongseong, the bitwised message information is encrypted. After applying the logistic map, bitwised information is reconstructed using the position of the chaotic sequence. The secret message is inserted into the randomly selected RGB channel. PSNR and SSIM were used to confirm the effectiveness of the applied results. It was confirmed as 44.392(dB) and 0.9884, respectively.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.17-26
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• 2019

We aimed to build a patient-based allergy prevention system using the smartphone and focused on the region of interest (ROI) extraction method for Optical Character Recognition (OCR) in the general environment. However, the current ROI extraction method has shown good performance in the experimental environment, but the performance in the real environment was not good due to the noisy background. Therefore, in this paper, we propose the compared methods of reducing noisy background to solve the ROI extraction problem. There five methods used as a SMF, DIN, Denoising Autoencoder(DAE), DAE with Convolution Neural Network(DAECNN) and median filter(MF) with DAECNN (MF+DAECNN). We have shown that our proposed DAECNN and MF+DAECNN methods are 69%, respectively, which is relatively higher than the conventional DAE method 55%. The verification of performance improvement uses MSE, PSNR and SSIM. The system has implemented OpenCV, C++ and Python, including its performance, is tested on real images.