• 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.

• 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 the Institute of Electronics Engineers of Korea SD
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• v.46 no.5
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• pp.39-48
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• 2009

Many super-resolution reconstruction algorithms have been proposed since it was the first proposed in 1984. The spatial domain approach of the super-resolution reconstruction methods is accomplished by mapping the low resolution image pixels into the high resolution image pixels. Generally, a super-resolution reconstruction algorithm by using the spatial domain approach has the noise problem because the low resolution images have different noise component, different PSF, and distortion, etc. In this paper, we proposed the new super-resolution reconstruction method that uses the L1 norm to minimize noise source and also uses the Huber norm to preserve edges of image. The proposed algorithm obtained the higher image quality of the result high resolution image comparing with other algorithms by experiment.

• Spatial Information Research
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• v.19 no.1
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• pp.29-40
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• 2011

GOCI the world first Ocean Color Imager in Geostationary Orbit, which could obtain total 8 images of the same region a day, however, its spatial resolution(500m) is not enough to use for the accurate land application, Super Resolution(SR), reconstructing the high resolution(HR) image from multiple low resolution(LR) images introduced by computer vision field. could be applied to the time-series remotely sensed images such as GOCI data, and the higher resolution image could be reconstructed from multiple images by the SR, and also the cloud masked area of images could be recovered. As the precedent study for developing the efficient SR method for GOCI images, on this research, it reproduced the simulated data under the acquisition process of the remote sensed data, and then the simulated images arc applied to the proposed algorithm. From the proposed algorithm result of the simulated data, it turned out that low resolution(LR) images could be registered in sub-pixel accuracy, and the reconstructed HR image including RMSE, PSNR, SSIM Index value compared with original HR image were 0.5763, 52.9183 db, 0.9486, could be obtained.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.575-586
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• 2022

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.409-412
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• 1996

Ultrasonic sensors are widely used in various applications due to advantages of low cost, simplicity in construction, mechanical robustness, and little environmental restriction in usage. But the main purposes of the noncontact sensing are rather narrowly confined within object detection and distance measurement. For the application of object recognition, ultrasonic sensors exhibit several shortcomings of poor directionality which results in low spatial resolution of objects, and specularity which gives frequent erroneous range readings. To resolve these problems in object recognition, an array of the sensor has been used. To improve the spatial resolution, more number of sensors are used in essence throughout the various devices of the sensor arrays. Under the disguise of a fixed number of the sensors, the array can be shifted mechanically in several steps. In this paper we propose a practical sensor resolution enhancement method using an electronic circuit accompanying the sensor array. The circuit changes the transmitter output voltage in several steps. Using the known sensor characteristics, a set of different return echo signals provide enhanced spatial resolution. The improvement is obtained with neither the cost of the increased number of the sensors nor extra mechanical devices.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.687-690
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• 1999

Many high-resolution algorithms based on the eigen-decomposition analysis of observed covariance matrix, such as MVE, MUSIC, and EVM, have been proposed. However, the resolution of spectral estimates for these algorithms is severely degraded when Signal-to-Noise Ratio (SNR) is low and arrival angles of signal are close to each other. And EVM and MUSIC is powerful for the characteristic of SNR. But have the limitation that the number of signals presented is known. While MVE is bad the characteristic of SNR. In this study, we propose a modified MVE to enhance the resolution for Direction-Of-Arrival (DOA) estimation of underwater acoustic signal. This is to remove the limitation that existing algorithms should know the information for the number of signals. Because the algorithms founded on the eigen value estimate DOA with only the noise subspace, they have the high-resolution characteristic. And then, with the method reducing the effect of the signal subspace, we are to reduce the degradation because of complementary relationship between the signal subspace and the noise subspace. This paper, with using the simulation data, we have estimated the proposed algorithms, compared it with other high-resolution algorithms. The simulation results show that the modified MVE proposed is accurate and has a better resolution even though SNR is low, under the same condition.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1393-1401
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• 2014

This paper presents an adaptive image interpolation method using a pixel-based neighbor embedding which is modified from the patch-based neighbor embedding of contemporary super resolution algorithms. Conventional interpolation methods for high resolution detect at least 16-directional edges in order to remove zig-zaging effects and selectively choose the interpolation strategy according to the direction and value of edge. Thus, they require much computation and high complexity. In order to develop a simple interpolation method preserving edge's directional shape, the proposed algorithm adopts the simplest Haar wavelet and suggests a new pixel-based embedding scheme. First, the low-quality image but high resolution, magnified into 1 octave above, is acquired using an adaptive 8-directional interpolation based on the high frequency coefficients of the wavelet transform. Thereafter, the pixel embedding process updates a high resolution pixel of the magnified image with the weighted sum of the best matched pixel value, which is searched at its low resolution image. As the results, the proposed scheme is simple and removes zig-zaging effects without any additional process.

• Korean Journal of Computational Design and Engineering
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• v.11 no.4
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• pp.315-325
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• 2006

It is very often necessary to search for similar parts during designing a new product because its parts are often easily designed by modifying existing similar parts. In this way, the design time and cost can be reduced. Thus it would be nice to have an efficient similarity comparison algorithm that can be used anytime in the design process. There have been many approaches to compare shape similarity between two solids. In this paper, two parts represented in B-Rep is compared in two steps: one for overall appearances and the other for detail features. In the first step, geometric information is used in low level of detail for easy and fast pre-classification by the overall appearance. In the second step, feature information is used to compare the detail shape in high level of detail to find more similar design. To realize the idea above, a multi resolution algorithm is proposed so that a given solid is described by an overall appearance in a low resolution and by detail features in high resolution. Using this multi-resolution representation, parts can be compared based on the overall appearance first so that the number of parts to be compared in high resolution is reduced, and then detail features are investigated to retrieve the most similar part. In this way, computational time can be reduced by the fast classification in the first step while reliability can be preserved by detail comparison in the second step.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2277-2288
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• 2021

To regulate the safety protocols in nuclear facilities, radioactive aerosols have been extensively researched to understand their health impacts. However, most measured particle-size distributions remain at low resolutions, with the particle sizes ranging from nanometer to micrometer. This study combines the high-resolution detection of 500 size classes, ranging from 6 nm to 10 ㎛, for aerodynamic diameter distributions, with a regional lung deposition calculation. We applied the new approach to characterize particle-size distributions of aerosols generated during the plasma arc cutting of simulated non-radioactive steel alloy wastes. The high-resolution measured data were used to calculate the deposition ratios of the aerosols in different lung regions. The deposition ratios in the alveolar sacs contained the dominant particle sizes ranging from 0.01 to 0.1 ㎛. We determined the distribution of various metals using different vapor pressures of the alloying components and analyzed the uncertainties of lung deposition calculations using the low-resolution aerodynamic diameter data simultaneously. In high-resolution data, the changes in aerosols that can penetrate the blood system were better captured, correcting their potential risks by a maximum of 42%. The combined calculations can aid the enhancement of high-resolution measuring equipment to effectively manage radiation safety in nuclear facilities.