• 한국융합학회논문지
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• 제9권2호
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• pp.119-124
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• 2018

증강현실 기술은 사용자의 시각에 가상의 영상 정보를 결합하여 다양한 영상 정보를 동시에 취득할 수 있게 한다. 이러한 증강현실 기술은 최근 의료융합을 통해 이미지 가이드 수술(Image-Guide Operation), 수술 교육 훈련, 영상 진단 분야에서 수술 및 진단 시 환자의 장기나 조직들을 구분하여 가시화하고 가장 효과적인 수술 방법을 제시 할 수 있다. 이에 본 논문을 통해 증강현실 기술의 의료 융합을 위한 각 요소 기술의 기술적 특징과 적용 방안 등을 고찰하고자 하였다. 의료융합을 위한 증강현실 기술에서는 효율적인 의료 영상의 구현을 위해 디스플레이, 마커인식 그리고 영상합성 인터페이스 기술의 유기적인 구동이 필수적이다. 이러한 증강현실 기술은 향후, 이미지 가이드 수술, 수술 교육 및 영상 진단 등의 분야에서 현재의 의료 기술을 획기적으로 증진시킬 수 있는 방안이 될 수 있으리라 여겨진다.

• International Journal of Advanced Culture Technology
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• 제10권2호
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• 반도체디스플레이기술학회지
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• 제10권3호
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• pp.89-94
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• 2011

The image identification methodology associates an image with a unique identifiable representation. Whenever the methodology regenerates an identifier for the same image, moreover, the newly created identifier needs to be consistent in terms of representation value. In this paper, we discuss a methodology for image identifier generation utilizing luminance correlation. We furthermore propose a method for performance enhancement of the image identifier generation. We also demonstrate the experimental evaluations for uniqueness and similarity analysis and performance improvement that have shown favorable results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권12호
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• pp.3149-3165
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• 2013

Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4420-4438
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• 2021

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.

• 대한공간정보학회지
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• 제10권5호
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• pp.45-51
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• 2002

The goal of image merging techniques are to enhance the resolution of low-resolution images using the detail information of the high-resolution images. Among the several image merging methods, wavelet-based image merging techniques have the advantages of efficient decorrelation of image bands and time-scale analysis. However, they have no regard for spatial information between the bands. In other words, multiresolution data merging methods merge the same information-the detail information of panchromatic image-with other band images, without considering specific characteristics. Therefore, a merged image contains much unnecessary information. In this paper, we discussed this 'mixing' effect and, proposed a method to classify the detail information of the panchromatic image according to the spatial and spectral characteristics, and to minimize distortion of the merged image.

• 한국항행학회논문지
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• 제20권6호
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• pp.625-630
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• 2016

최근 다양한 형태의 영상 콘텐츠가 제작되고 있고, 이를 위한 저작권 보호기술에 대한 관심이 높아지고 있다. 본 논문에서는 컬러 영상을 위한 영상암호화 기술을 제안한다. 이 기술은 영상을 RGB 색차성분으로 분할한 후, 윤곽선을 기준으로 쿼드트리 분할을 수행한다. 쿼드트리 분할 후, 선정된 블록들을 대상으로 암호화를 수행한다. 색차성분을 대상으로 암호화를 수행하여 암호화 효율을 측정하고, 컬러 영상으로 재구성한 후에도 암호화 효율을 측정한다. 암호화 효율은 시각적인 측정방식과 객관적인 화질평가 방식을 이용한다. 실험결과 PSNR 수치는 색차성분의 경우 7~10[dB], 컬러 영상의 경우 16~19[dB]로 측정되었다. 본 논문에서 제안한 영상암호화 기술은 향후 다양한 디지털 영상 콘텐츠의 저작권 보호에 사용될 것으로 보인다.

• Journal of information and communication convergence engineering
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• 제8권5호
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• pp.595-601
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• 2010

The strategy through visual sense is one of the ways that subject builds the outside world and communicates. The visual sense seems higher level of dependence than the other senses and contributes to intercommunication. For this reason, the desire of image dates back to primitive art and visual image(visual media and visual culture) has dialectically developed in the history of mankind. Visual subject, based on perspective of Renaissance, was moved from God to human beings. Andre Bazin's 'la genese automatique' through technical art has epochally changed the paradigm of visual art and visual culture. From primitive art to photo and film, the image, based on visual sense, has reflected human wish, appealed visual desire and led to evolution of image. In the late 20 century, without dialectical evolution of technology and culture, rapidly progressive digital image has changed social and cultural implication over rational strategy of production and distribution and it strengthened authority of image through visual approach of endless desire. The goal of this study is to analyze the value change of digital image developing a new Renaissance through production, reading, communication, and implication of evolution due to digitalized image, which has evolved as object and tool of desire.

• Journal of Electrical Engineering and Technology
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• 제10권6호
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• pp.2384-2392
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• 2015

The technical development and practical applications of big-data for health is one hot topic under the banner of big-data. Big-data medical image fusion is one of key problems. A new fusion approach with coding based on Spherical Coordinate Domain (SCD) in Wireless Sensor Network (WSN) for big-data medical image is proposed in this paper. In this approach, the three high-frequency coefficients in wavelet domain of medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on the multi-scale edge of medical image, it can be fused and reconstructed. Experimental results indicate the novel approach is effective and very useful for transmission of big-data medical image(especially, in the wireless environment).

• Current Optics and Photonics
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• 제8권3호
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• pp.270-281
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• 2024

Information-hiding technology is introduced into an optical ghost imaging encryption scheme, which can greatly improve the security of the encryption scheme. However, in the current mainstream research on camouflage ghost imaging encryption, information hiding techniques such as digital watermarking can only hide 1/4 resolution information of a cover image, and most secret images are simple binary images. In this paper, we propose an equal-resolution image-hiding encryption scheme based on deep learning and computational ghost imaging. With the equal-resolution image steganography network based on deep learning (ERIS-Net), we can realize the hiding and extraction of equal-resolution natural images and increase the amount of encrypted information from 25% to 100% when transmitting the same size of secret data. To the best of our knowledge, this paper combines image steganography based on deep learning with optical ghost imaging encryption method for the first time. With deep learning experiments and simulation, the feasibility, security, robustness, and high encryption capacity of this scheme are verified, and a new idea for optical ghost imaging encryption is proposed.