• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.703-712
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• 2019

Single image Super-Resolution (SISR) aims to generate a visually pleasing high-resolution image from its degraded low-resolution measurement. In recent years, deep learning - based super - resolution methods have been actively researched and have shown more reliable and high performance. A typical method is WaveletSRNet, which restores high-resolution images through wavelet coefficient learning based on feature maps of images. However, there are two disadvantages in WaveletSRNet. One is a big processing time due to the complexity of the algorithm. The other is not to utilize feature maps efficiently when extracting input image's features. To improve this problems, we propose an efficient single image super resolution method, named RDB-WaveletSRNet. The proposed method uses the residual dense block to effectively extract low-resolution feature maps to improve single image super-resolution performance. We also adjust appropriated growth rates to solve complex computational problems. In addition, wavelet packet decomposition is used to obtain the wavelet coefficients according to the possibility of large scale ratio. In the experimental result on various images, we have proven that the proposed method has faster processing time and better image quality than the conventional methods. Experimental results have shown that the proposed method has better image quality by increasing 0.1813dB of PSNR and 1.17 times faster than the conventional method.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.5-18
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• 2023

As the development of drones and sensors accelerates, new services and values are created by fusing data acquired from various sensors mounted on drone. However, the construction of spatial information through data fusion is mainly constructed depending on the image, and the quality of data is determined according to the specification and performance of the hardware. In addition, it is difficult to utilize it in the actual field because expensive equipment is required to construct spatial information of high-quality. In this study, super-resolution was performed by applying deep learning to low-resolution images acquired through RGB and THM cameras mounted on a drone, and quantitative evaluation and feature point extraction were performed on the generated high-resolution images. As a result of the experiment, the high-resolution image generated by super-resolution was maintained the characteristics of the original image, and as the resolution was improved, more features could be extracted compared to the original image. Therefore, when generating a high-resolution image by applying a low-resolution image to an super-resolution deep learning model, it is judged to be a new method to construct spatial information of high-quality without being restricted by hardware.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.1
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• pp.17-26
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• 2012

This paper presents an image processing-based validation method for unrecognizable numbers in severely distorted license plate images which have been degraded by various factors including low-resolution, low light-level, geometric distortion, and periodic noise. Existing vehicle license plate recognition (LPR) methods assume that most of the image degradation factors have been removed before performing the recognition of printed numbers and letters. If this is not the case, conventional LPR becomes impossible. The proposed method adopts a novel approach where a set of reference number images are intentionally degraded using the same factors estimated from the input image. After a series of image processing steps, including geometric transformation, super-resolution, and filtering, a comparison using cross-correlation between the intentionally degraded reference and the input images can provide a successful identification of the visually unrecognizable numbers. The proposed method makes it possible to validate numbers in a license plate image taken under low light-level conditions. In the experiment, using an extended set of test images that are unrecognizable to human vision, the proposed method provides a successful recognition rate of over 95%, whereas most existing LPR methods fail due to the severe distortion.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.257-264
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• 2009

This paper proposes a novel face detection method that finds tiny faces located at a long range even with low-resolution input images captured by a mobile robot. The proposed approach can locate extremely small-sized face regions of $12{\times}12$ pixels. We solve a tiny face detection problem by organizing a system that consists of multiple detectors including a mean-shift color tracker, short- and long-rage face detectors, and an omega shape detector. The proposed method adopts the long-range face detector that is well trained enough to detect tiny faces at a long range, and limiting its operation to only within a search region that is automatically determined by the mean-shift color tracker and the omega shape detector. By focusing on limiting the face search region as much as possible, the proposed method can accurately detect tiny faces at a long distance even with a low-resolution image, and decrease false positives sharply. According to the experimental results on realistic databases, the performance of the proposed approach is at a sufficiently practical level for various robot applications such as face recognition of non-cooperative users, human-following, and gesture recognition for long-range interaction.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.5
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• pp.628-636
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• 1999

This work describes the low-resolution spectral modeling of the water vapor, carbon dioxide and their mixtures by applying the weighted-sum-of-gray-gas-gases model (WSGGM) to each narrow band. Proper modeling scheme of gray gas absorption coefficients vs temperature relation is suggested. Comparison between the modeled emissivity calculated from this relation and the 'true' emissivity obtained from the high temperature statistical narrow band parameters is made for a few typical narrow bands. Low resolution spectral intensities from one-dimensional layers are also obtained and examined for uniform, parabolic and boundary layer type temperature profiles using the obtained WSGGM's with several gray gases. The results are compared with the narrow band spectral intensities obtained by a narrow band model-based code with Curtis-Godson approximation. Good agreement is found between them. Data bases including optimized modeling parameters and total and low-resolution spectral weighting factors are developed for water vapor, carbon dioxide and their mixtures. This model and obtained data bases, available from the authors' Internet site, can be appropriately applied to any radiative transfer equation solver.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2617-2618
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• 2002

The key point of this design can offer good picture resolution and a high-speed color STN decode. We maximize a combination processing of the color signal. Therefore, there is a color implementation at the natural. The age of the multimedia comes, so the color is considered important and wide. To be necessary a color implementation have become the importance which picture resolution is clean and low price of the liquid display TFT occupied greater part of the liquid display. But TFT could not consist low price realization. This research is a color implementation of STN at low price, design good picture resolution decode for optimum an limit of upside with frequency range maximizing and can reply in the high-speed by multidivision method. As a result, we design optimum of the STN decode.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.6
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• pp.618-625
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• 2005

This paper has achieved that an optimal design and experiments of heater and cathode of electron gun that serve to embody high current density in CRT display. For the high brightness, high resolution and larger size in CRT display, high current density of electron gun is indispensible. An Impregnation style cathode is used, and must heighten operating temperature of heater to get high current density for this, it is proportional hereupon and power dissipation increases. In this paper, to get low power cathode with high current density, There are produced and tested sample that differ lead type of heater, coating method, the pitch and number of winding of the first and second coiling in the heat emission area for the low power design of high current density cathode heater in this paper.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.75-80
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• 2021

Artificial intelligence is becoming an important part of our lives providing incredible benefits. In this respect, facial expression recognition has been one of the hot topics among computer vision researchers in recent decades. Classifying small dataset of low resolution images requires the development of a new small scale deep CNN model. To do this, we propose a method suitable for small datasets. Compared to the traditional deep CNN models, this model uses only a fraction of the memory in terms of total learnable weights, but it shows very similar results for the FER2013 and FERPlus datasets.

• International Journal of Vascular Biomedical Engineering
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• v.2 no.2
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• pp.27-32
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• 2004

The objective of the present study is to visualize the steady and pulsatile flow fields in a branching model by using a high-resolution PIV system. A bifurcated flow system was built for the experiments in the steady and pulsatile flows. Harvard pulsatile pump was used to generate the pulsatile velocity waveforms. Conifer powder as the tracing particles was added to water to visualize the flow fields. CCD cameras($1K{\times}1K$(high resolution camera) and $640{\times}480$(low resolution camera)) captured two consecutive particle images at once for the image processing of several cross sections on the flow system. The range validation method and the area interpolation method were used to obtain the final velocity vectors with high accuracy. The results of the image processing clearly showed the recirculation zones and the formation of the paired secondary flows from the distal to the apex of the branch flow in the bifurcated model. The results also indicated that the particle velocities at the inner wall moved faster than the velocities at the outer wall due to the inertial force effects and the helical motions generated in the branch flows as the flow proceeded toward the outer wall. Even though the PIV images from the high resolution camera were closer to the simulation results than the images from the low resolution camera at some locations, both results of the PIV experiments from the two cameras generally agreed quite well with the results from the computer simulations. Therefore, instead of using the expensive stereoscopic PIV or 3D PIV system, the three-dimensional flow fields in a bifurcated model could be easily and exactly investigated by this study.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.71-77
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• 2011

In this paper, we propose a learning based super resolution algorithm using local block for image enhancement of vehicle license plate. Local block is defined as the minimum measure of block size containing the associative information in the image. Proposed method essentially generates appropriate local block sets suitable for various imaging conditions. In particular, local block training set is first constructed as ordered pair between high resolution local block and low resolution local block. We then generate low resolution local block training set of various size and blur conditions for matching to all possible blur condition of vehicle license plates. Finally, we perform association and merging of information to reconstruct into enhanced form of image from training local block sets. Representative experiments demonstrate the effectiveness of the proposed algorithm.