• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.8
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• pp.98-103
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• 2019

A more accurate method of feature point extraction and matching for three-dimensional reconstruction using low-resolution images of crops is proposed herein. This method is important in basic computer vision. In addition to three-dimensional reconstruction from exact matching, map-making and camera location information such as simultaneous localization and mapping can be calculated. The results of this study suggest applicable methods for low-resolution images that produce accurate results. This is expected to contribute to a system that measures crop growth condition.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.209-216
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• 2018

High-resolution satellite imagery is used in diverse fields such as meteorological observation, topography observation, remote sensing (RS), military facility monitoring and protection of cultural heritage. In satellite imagery, low-resolution imagery can take place depending on the conditions of hardware (e.g., optical system, satellite operation altitude, image sensor, etc.) even though the images were obtained from the same satellite imaging system. Once a satellite is launched, the adjustment of the imaging system cannot be done to improve the resolution of the degraded images. Therefore, there should be a way to improve resolution, using the satellite imagery. In this study, a super resolution (SR) algorithm was adopted to improve resolution, using such low-resolution satellite imagery. The SR algorithm is an algorithm which enhances image resolution by matching multiple low-resolution images. In satellite imagery, however, it is difficult to get several images on the same region. To take care of this problem, this study performed the SR algorithm by calibrating geometric changes on images after applying automatic extraction of feature points and projection transform. As a result, a clear edge was found just like the SR results in which feature points were manually obtained.

• Journal of Sensor Science and Technology
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• v.19 no.2
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• pp.118-123
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• 2010

This paper proposes super-resolution reconstruction algorithm for image enhancement. Super-resolution reconstruction algorithms reconstruct a high-resolution image from multi-frame low-resolution images of a scene. Conventional super- resolution reconstruction algorithms are iterative back-projection(IBP), robust super-resolution(RS)method and standard Papoulis-Gerchberg(PG)method. However, traditional methods have some problems such as rotation and ringing. So, this paper proposes modified algorithm to improve the problem. Experimental results show that this proposed algorithm solve the problem. As a result, the proposed method showed an increase in the PSNR for traditional super-resolution reconstruction algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.105-111
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• 2014

Image resolution enhancement is a technique to generate high-resolution image through improving resolution of low-resolution obtained image. It is important to estimate correctly missing pixel value in low-resolution obtained image for image resolution enhancement. In this paper, multiple shortfall estimation method for image resolution enhancement is proposed. The proposed method estimate separate multiple shortfall by predictive degradation-restoration processing in sub-images of obtained image, and generate result image combining the estimated shortfall and interpolated obtained-image. Lastly, final reconstruction image is generated by deblurring of the result image. The experimental results demonstrate that the proposed method has the best results of all compared methods in objective image quality index: PSNR, SSIM, and FSIM. The quality of reconstructed image is superior to all compared methods, and the proposed method has better lower computational complexity than compared methods. The proposed method can be useful for image resolution enhancement.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.192-201
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• 2010

This research deals with expressive definition of visual contents by using the distance between a subject and a screen resolution, and what changes affect the emotion of those looking at the expressive definition. A visual image captured from a HDTV screen was shown to the 61 students attending a university in the Busan area and SAM evaluation method was used to measure 3 different emotions such as pleasant, arousal, and dominance. While comparing different resolution, looking at high resolution contents rather than low resolution resulted in a direction of pleasant, arousal, and dominance. Also showing a different resolution than consistently showing the same resolution had a more volatile emotional effect. Aftermath multiple comparison resulted in a tendency for emotions to become unpleasant and un-arousal when high resolution contents were shown and then switched to a low resolution contents. There was no result of any significance in the control variables. Also on the aftermath multiple comparison on short, medium and long distance between the subject and the screen resolution, short distance had a bigger pleasant, arousal, and dominance emotional numbers than the rest. In a multiple variable verification result, a resolution and the distance of happiness and excitement showed a positive correlation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• The Journal of the Korea Contents Association
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• v.11 no.1
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• pp.236-244
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• 2011

Recently, the number of interventional procedures has increased dramatically as an alternative of invasive surgical procedure. The need for the quality control program of the angiographic units has also increased, because of concerns about the increased patient dose and the importance of image quality of angiographic units for the successful procedures. The purpose of this study was to propose an optimal guideline for the quality control program of the angiographic units. We reviewed domestic and international standards about medical imaging system and we evaluated the quality of 61 angiographic units in Korea with the use of NEMA 21 phantom. According to the results of our study, we propose a guideline for the quality control program of the angiographic units. Quality control program includes tube voltage test, tube current test, HVL test, image-field geometry test, spatial resolution test, low-contrast iodine detectability test, wire resolution test, phantom entrance dose test. Proposed reference levels are as follows: PAE < $\pm$ 10% in tube voltage test, PAE < $\pm$ 15% in tube current test, minimum 2.3 mmAl at 80 kVp in HVL test, minimum 'acceptable' level at image-field geometry test, 0.8 lp/mm for detector size of 34-40cm, 1.0 lp/mm for detector size of 28-33cm, 1.2 lp/mm for detector size of 22-27cm in spatial resolution test, minimum 200mg/cc in low contrast iodine detectability test, phantom entrance dose should be under 10R/min, 0.012 inch wire should be seen at static wire resolution test, and 0.022 inch wire should be seen at moving wire resolution test.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.119-123
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• 2004

This paper describes low temperature poly-Si (LTPS) TFT system on glass (SOG) technology developed in NEC. High resolution SOG-LCDs such as a 230 ppi reflective type LCD, a 2.5", 333 ppi 2D/3D autostereoscopic LCD, and a 2.1" single voltage driven full integration LCD for mobile applications and a 0.9", XGA light valve for projectors are reviewed from the perspective of the high resolution technologies

• Imaging Science in Dentistry
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• v.48 no.1
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• pp.11-19
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• 2018

Purpose: This study compared the diagnostic accuracy of cone-beam computed tomography (CBCT) scans obtained with 2 CBCT systems with high- and low-resolution modes for the detection of root perforations in endodontically treated mandibular molars. Materials and Methods: The root canals of 72 mandibular molars were cleaned and shaped. Perforations measuring 0.2, 0.3, and 0.4 mm in diameter were created at the furcation area of 48 roots, simulating strip perforations, or on the external surfaces of 48 roots, simulating root perforations. Forty-eight roots remained intact(control group). The roots were filled using gutta-percha (Gapadent, Tianjin, China) and AH26 sealer (Dentsply Maillefer, Ballaigues, Switzerland). The CBCT scans were obtained using the NewTom 3G (QR srl, Verona, Italy) and Cranex 3D (Soredex, Helsinki, Finland) CBCT systems in high- and low-resolution modes, and were evaluated by 2 observers. The chi-square test was used to assess the nominal variables. Results: In strip perforations, the accuracies of low- and high-resolution modes were 75% and 83% for NewTom 3G and 67% and 69% for Cranex 3D. In root perforations, the accuracies of low- and high-resolution modes were 79% and 83% for NewTom 3G and was 56% and 73% for Cranex 3D. Conclusion: The accuracy of the 2 CBCT systems was different for the detection of strip and root perforations. The Cranex 3D had non-significantly higher accuracy than the NewTom 3G. In both scanners, the high-resolution mode yielded significantly higher accuracy than the low-resolution mode. The diagnostic accuracy of CBCT scans was not affected by the perforation diameter.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.242-251
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• 2020

Recently, many deep convolutional neural networks for image super-resolution have been studied. Existing deep learning-based super-resolution algorithms are architecture that up-samples the resolution at the end of the network. The post-upsampling architecture has an inefficient structure at large scaling factor result of predicting a lot of information for mapping from low-resolution to high-resolution at once. In this paper, we propose a single image super-resolution using Channel Attention Residual Dense Block based on an iterative up-down sampling architecture. The proposed algorithm efficiently predicts the mapping relationship between low-resolution and high-resolution, and shows up to 0.14dB performance improvement and enhanced subjective image quality compared to the existing algorithm at large scaling factor result.