• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.197-200
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• 2005

Super resolution image reconstruction method refers to image processing algorithms that produce a high resolution(HR) image from observed several low resolution(LR) images of the same scene. This method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, such as satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. In this paper we applied super resolution reconstruction method in spatial domain to video sequences. Test images are adjacently sampled images from continuous video sequences and overlapped for high rate. We constructed the observation model between the HR images and LR images applied by the Maximum A Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from low resolution images and compared the results with those from other known interpolation methods.

• Journal of Space Technology and Applications
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• v.3 no.3
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• pp.257-279
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• 2023

Korea Aerospace Research Institute has developed 6U Nano-Satellite high resolution video and image (HiREV) for the purpose of developing core technology for deep space exploration. The 6U HiREV Nano-Satellite has a mission of high-resolution image and video for earth observation, and the thermal pointing error between the lens and the camera module can occur due to the high temperature in camera module on mission mode. The thermal pointing error has a large effect on the resolution, so thermal design should solve it because the HiREV optical camera is developed based on commercial products that are the industrial level. So, when it operates in space, the thermal design is needed, because it has the best performance at room temperature. In this paper, three passive thermal designs were performed for the camera mission payload, and the thermal design was proved to be effective by performing on-orbit thermal analysis.

• Journal of Broadcast Engineering
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• v.21 no.5
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• pp.651-661
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• 2016

최근의 디지털 사이니지는 대형 고해상도 디스플레이를 이용해 사용자에게 초고해상도 파노라마형태의 볼거리를 제공하거나 사용자와 인터렉션 등을 통해 맞춤형광고를 제공하는 등 다양한 형태로 발전하고 있다. 또 개방형 디지털 사이니지의 발전과 함께 대형 멀티 사이니지를 이용한 초고화질 영상콘텐츠를 다양한 서비스 단말장치의 연동과 웹기반의 상호운용성을 갖춘 관리시스템이 활발히 연구될 것으로 보인다. 본 논문에서는 수십대에서 백여대 이상의 고해상도 디스플레이를 연동하여 초고해상도 영상표출의 기술의 구현방안과 정형화된 영상 해상도가 아닌 초고화질 영상의 개별 콘텐츠에 대한 동기화 시키는 기술을 이용하여 멀티 콘텐츠 플레이 단말기에서 고해상도 영상을 Play Sync하는 기술을 연구한다.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.132-141
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• 2019

The necessity of transmitting video data over a narrow-bandwidth exists steadily despite that video service over broadband is common. In this paper, we propose a scalable video coding framework for low-resolution video transmission over a very narrow-bandwidth network by super-resolution of decoded frames of a base layer using a convolutional neural network based super resolution technique to improve the coding efficiency by using it as a prediction for the enhancement layer. In contrast to the conventional scalable high efficiency video coding (SHVC) standard, in which upscaling is performed with a fixed filter, we propose a scalable video coding framework that replaces the existing fixed up-scaling filter by using the trained convolutional neural network for super-resolution. For this, we proposed a neural network structure with skip connection and residual learning technique and trained it according to the application scenario of the video coding framework. For the application scenario where a video whose resolution is $352{\times}288$ and frame rate is 8fps is encoded at 110kbps, the quality of the proposed scalable video coding framework is higher than that of the SHVC framework.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.115-118
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• 2004

Images with high resolution are desired and often required in many visual applications. When resolution can not be improved by replacing sensors, either because of cost or hardware physical limits, super resolution image reconstruction method is what can be resorted to. Super resolution image reconstruction method refers to image processing algorithms that produce high quality and high resolution images from a set of low quality and low resolution images. The method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. The method can be either the frequency domain approach or the spatial domain approach. Much of the earlier works concentrated on the frequency domain formulation, but as more general degradation models were considered, later researches had been almost exclusively on spatial domain formulations. The method in spatial domains has three stages: i) motion estimate or image registration, ii) interpolation onto high resolution grid and iii) deblurring process. The super resolution grid construction in the second stage was discussed in this paper. We applied the Maximum A­Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from a set of low resolution images and compared the results with those from other known interpolation methods.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.345-354
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• 2005

Super resolution image reconstruction method refers to image processing algorithms that produce a high resolution(HR) image from observed several low resolution(LR) images of the same scene. This method has proven to be useful in many practical cases where multiple frames of the same scene can be obtained, such as satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. In this paper, we applied the super resolution reconstruction method in spatial domain to video sequences. Test images are adjacently sampled images from continuous video sequences and are overlapped at high rate. We constructed the observation model between the HR images and LR images applied with the Maximum A Posteriori(MAP) reconstruction method which is one of the major methods in the super resolution grid construction. Based on the MAP method, we reconstructed high resolution images from low resolution images and compared the results with those from other known interpolation methods.

• Journal of Korea Multimedia Society
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• v.21 no.7
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• pp.795-801
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• 2018

In these days, various technologies to provide a service of high quality of 360 VR media contents is being studied and developed. However, rendering high-quality of media images is very difficult with the limited resources of HMD (Head Mount Display). In this paper, we designed and implemented a 360 VR Player for high quality 360 tiled video image render to HMD. Furthermore, we developed multi-resolution-based Foveated Rendering technology. By conducting several experiments, We have confirmed that it improved the performance of video rendering far more than existing tiled video rendering technology.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.391-393
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• 2006

In this paper, we develop a portable game device with uncompressed HD video and high quality sound output. Portable game devices support not only game function but also various complex functions recently. It especially supports TV-Out port to play realistic game, connecting a large screen display device. But the video and audio output signals of conventional TV-out port have the low performance and these signals are analog output. So, it is difficult that the users enjoy realistic game with benefit of high resolution digital TV. We propose the game device output with uncompressed digital signal, which has no delay of video/audio signal, also has strong immunity to external noise. Since it supports a high resolution video and high quality sound, users can playa realistic game. First, we implement the HDMI to the game device and we test reliability with the various resolutions video inputs and audio inputs. The proposed method can be applied multimedia devices requiring high performance output function as well as portable devices.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1467-1472
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• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.12-18
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• 2006

This paper presents a method to extract a good high-resolution image from a low-resolution video in an automatic manner. Since a high-resolution image reconstruction method utilizing several low-resolution input images works better than a conventional interpolation method utilizing single low-resolution input image only if the input images are well registered onto a common high-resolution grid, low-resolution input images should be carefully chosen so that the registration errors can be carefully considered. In this paper, the statistics obtained from the motion-compensated low-resolution images are utilized to evaluate the feasibility of the input image candidates. Maximum motion-compensation error is estimated from the high-resolution image observation model. U the motion-compensation error of the input image candidate is greater than the estimated maximum motion-compensation error, the input image candidate is discarded. The number of good input image candidates and the statistics of the motion-compensation errors are used to choose final input images. The final input images chosen from the input image selection block are given to the following high-resolution image reconstruction block. It is expected that the proposed method is utilized to extract a good high-resolution image efficiently from a low-resolution video without any user intervention.