• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.29-38
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• 2022

Although image compression is one of the essential technologies to transmit image data on a variety of surveillance and mobile healthcare applications, it causes unnecessary compression artifacts such as blocking and ringing artifacts by the lossy compression in the limited network bandwidth. Recently, image restoration methods using convolutional neural network (CNN) show the significant improvement of image quality from the compressed images. In this paper, we propose Image Denoising Convolutional Neural Networks (IDCNN) to reduce the compression artifacts for the purpose of improving the performance of object classification. In order to evaluate the classification accuracy, we used the ImageNet test dataset consisting of 50,000 natural images and measured the classification performance in terms of Top-1 and Top-5 accuracy. Experimental results show that the proposed IDCNN can improve Top-1 and Top-5 accuracy as high as 2.46% and 2.42%, respectively.

• The Transactions of the Korea Information Processing Society
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• v.2 no.4
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• pp.524-534
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• 1995

Image segmentation is a process of partitioning of an image into different regions that have similar properties. It is an old and difficult problem. They have developed many image segmentation systems and have studied to evaluate the existing segmentation systems. However, evaluation of image segmentation systems is very difficult in nature. In this paper, we propose the evaluation criteria that evaluate image segmentation systems automatically. In order to overcome the drawbacks of using only a single evaluation criterion, we have incorporated four evaluation criteria, that is, boundary difference, boundary consistency, region uniformity, and region difference. As the experimental results show, our evaluation criteria performs very well.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.2
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• pp.165-172
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• 2020

There is growing interest in Vision-based video image matching owing to the constantly developing technology of unmanned-based systems. The purpose of this paper is the development of a fast and effective matching technique for the UAV oblique video image. We first extracted initial matching points using NCC (Normalized Cross-Correlation) algorithm and improved the computational efficiency of NCC algorithm using integral image. Furthermore, we developed a triangulation-based outlier removal algorithm to extract more robust matching points among the initial matching points. In order to evaluate the performance of the propose method, our method was quantitatively compared with existing image matching approaches. Experimental results demonstrated that the proposed method can process 2.57 frames per second for video image matching and is up to 4 times faster than existing methods. The proposed method therefore has a good potential for the various video-based applications that requires image matching as a pre-processing.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.6-9
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• 2012

In this study, we compare two image denoising methods which are the Rudin-Osher-Fatemi total variation (TV) model and the nonlocal means (NLM) algorithm on medical images. To evaluate those methods, we used two well known measuring metrics. The methods are tested with a CT image, one X-Ray image, and three MRI images. Experimental result shows that the NML algorithm can give better results than the ROF TV model, but computational complexity is high.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.572-576
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• 1997

X-ray laminography and DT(digital tomosynthesis) that can form a cross-sectional image of 3-D objects promise to be good solutions for inspecting interior defects of industrial products. The major factors of the digital tomosynthesis that influence on the quality of x-ray cross-sectional images are also discussed. The quality of images acquired from the DT system varies according to image synthesizing methods, the number of images used in image synthesizing, and X-ray projection angles. In this paper, a new image synthesizing method named 'log-root method' is proposed to get clear and accurate cross-sectional images, which can reduce both artifact and blurring generated by materials out of focal plane. To evaluate the quality of cross-sectional images, two evaluating criteria: (1) shape accuracy and (2) clearness in the cross-sectional image are defined. Based on this criteria, a series of simulations were performed, and the results show the superiority of the new synthesizing method over the existing ones such as averaging and minimum method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.471-478
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• 2011

High resolution Pan-sharpening technique is becoming increasingly important in the field of remote sensing image analysis as an essential image processing to improve the spatial resolution of original multispectral image. The general scheme of pan-sharpening technique consists of upsampling process of multispectral image and high-pass detail injection process using the panchromatic image. The upsampling process, however, brings about image blurring, and this lead to spectral distortion in the pan-sharpening process. In order to solve this problem, this paper presents a new method that adopts image restoration techniques based on optimization theory in the pan-sharpening process, and evaluates its efficiency and application possibility. In order to evaluate the effect of image restoration techniques on the pansharpening process, the result obtained using the existing method that used bicubic interpolation were compared visually and quantitatively with the results obtained using image restoration techniques. The quantitative comparison was done using some spectral distortion measures for use to evaluate the quality of pan-sharpened image.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.8
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• pp.904-912
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• 2003

This study for‘Head Image’, which is affected by individual Image, is via fashion collection to analyze formative feature, fashion emotion and meaning structure of emotion and to inquire into correlation. I will offer fundamental data, which is can use Image making from the state of thing. First, to make charm and personal image, if we consider Head image well, it will very effective by the reason that personality and charm operate as important factors in fashion sensibility of Head Image. Second, we can know Head Image has more strong influence the part of emotion than fashion sensibility by showing that the sense of emotion is higher than this point of view of fashion sensibility in Head Image. Third, As a result of the correlation of fashion sensibility and emotion in Head Image, personal Head Image is effective to attract public gaze by causing negative emotion, and attractive Head Image is effective to give pleasant feeling by causing positive emotion. Forth, Avant-garde, Punk, Kitsch Image were estimated as the most personal things and Romantic, Ethnic Image were estimated as the most attractive things of the type of Head Image. Natural Image was estimated as the most feminine thing, and Elegant Image was estimated as the most mature thing. Fifth, when we look into the different appraisals between experts and amateurs about fashion sensibility and emotion of Head Image, a selection of experts are used to peculiar and strong Head Image, so amateurs respond it more sensitively and highly evaluate.

• International Journal of Contents
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• v.11 no.2
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• pp.20-30
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• 2015

We propose a new method that specifies objective image quality factors by evaluating an image quality measurement model using random images. In other words, No-Reference variables are used to evaluate the quality of an original image without using any reference for comparison. 1000 portrait images were collected from a web gallery with votes constituting over 30 recommendation values. The bottom-up data collecting process was used to calculate the following image quality factors: total range, average, standard deviation, normalized distribution, z-score, preference percentage. A final grade is awarded out of 100 points, and this method ranks and grades the final estimated image quality preference in terms of total image quality factors. The results of the proposed image quality evaluation model consist of the specific dynamic range, skin tone R, G, B, L, A, B, and RSC contrast. We can present the total for the expected preference points as the average of the objective image qualities. Our proposed image quality evaluation model can measure the preferences for an actual image using a statistical analysis. The results indicate that this is a practical image quality measurement model that can extract a subject's preferred image quality.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.2
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• pp.26-38
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• 2021

Image coloration refers to adding plausible colors to a grayscale image or video. Image coloration has been used in many modern fields, including restoring old photographs, as well as reducing the time spent painting cartoons. In this paper, a method is proposed for colorizing grayscale images using a convolutional neural network. We propose an encoder-decoder model, adapting FusionNet to our purpose. A proper loss function is defined instead of the MSE loss function to suit the purpose of coloring. The proposed model was verified using the ImageNet dataset. We quantitatively compared several colorization models with ours, using the peak signal-to-noise ratio (PSNR) metric. In addition, to qualitatively evaluate the results, our model was applied to images in the test dataset and compared to images applied to various other models. Finally, we applied our model to a selection of old black and white photographs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.13-20
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• 2010

We proposed a new technique to perform fast video stabilization using integral image in this article. In the proposed technique, it evaluate local and global motion by the block matching using the generated integral image for each frame and compensate the motion like jitter. We made the various experimental jitter patterns to evaluate the effectiveness of the proposed technique and evaluated stabilization capability and execution time with the existing ones. Through the experiment, we found that the execution time of proposed technique was faster than that of existing techniques and the compensation of jitter was well done.