• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1124-1132
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• 2009

We proposed a new method to improve the resolution of elemental image set in the integral imaging system using super-resolution image reconstruction method. Adjacent elemental images have same image region which is projected from the common area of object. These projected images in the elemental image can be used for low resolution images of super-resolution method. Two methods for resolution improvement of elemental image set using super-resolution method are proposed. One is super-resolution among the elemental image sets and the other is among the elemental images. Simulation results are compared with resolution improved elemental image set using interpolated method.

• Journal of Digital Policy
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• v.1 no.2
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• pp.53-59
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• 2022

In this paper, we proposed a method using sub-edge information extracted through a hierarchical structure in the process of generating super resolution based on a single image. In order to improve the quality of super resolution, it is necessary to clearly distinguish the shape of each area while clearly expressing the boundary area in the image. The proposed method assists edge information of the image in deep learning based super resolution method to create an improved super resolution result while maintaining the structural shape of the boundary region, which is an important factor determining the quality in the super resolution process. In addition to the group convolution structure for performing deep learning based super resolution, a separate hierarchical edge accumulation extraction process based on high-frequency band information for sub-edge extraction is proposed, and a method of using it as an auxiliary feature is proposed. Experimental results showed about 1% performance improvement in PSNR and SSIM compared to the existing super resolution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.774-784
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• 2021

The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.559-570
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• 2022

When a satellite image has low spatial resolution, it is difficult to detect small objects. In this research, we aim to check the effect of super resolution on object detection. Super resolution is a software method that increases the resolution of an image. Unpaired super resolution network is used to improve Sentinel-2's spatial resolution from 10 m to 3.2 m. Faster-RCNN, RetinaNet, FCOS, and S²ANet were used to detect vessels in the Sentinel-2 images. We experimented the change in vessel detection performance when super resolution is applied. As a result, the Average Precision (AP) improved by at least 12.3% and up to 33.3% in the ship detection models trained with the super-resolution image. False positive and false negative cases also decreased. This implies that super resolution can be an important pre-processing step in object detection, and it is expected to greatly contribute to improving the accuracy of other image-based deep learning technologies along with object detection.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.15-22
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• 2020

In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.72-79
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• 2007

In this paper, It propose a new restoration algorithm of high resolution, which is reconstructed to high resolution image using low resolution image informations. The proposed algorithm is constructed based on super resolution theory, it is consisted of progressive steps of the integration and construction. It reduced a lot of data-processing capacity and noise with integration through sub-pixel movement and wavelet basis through a higher resolution. As a result, it is shown that the main information is maintained and the error rate is improved. Using expansion fuzzy wavelet B-spline interpolation in stage of construction, it is confirmed that we can achieve smoothing image and good resolution without blur and block.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6A
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• pp.464-472
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• 2012

In this paper, we propose a new super-resolution algorithm that uses successive frames by applying the block matching motion estimation algorithm. Usually, single frame super-resolution algorithms are based on probability or discrete wavelet transform (DWT) approach to extract high-frequency components of the input image, but only limited information is available for these algorithms. To solve this problem, various multiple-frame based super-resolution algorithms are proposed. The accuracy of registration between frames is a very important factor for the good performance of an algorithm. We therefore propose an algorithm using 6-Tap FIR filter to increase the accuracy of the image registration with sub-pixel unit. Proposed algorithm shows better performance than other conventional interpolation based algorithms such as nearest neighborhood, bi-linear and bi-cubic methods and results in about the same image quality as DWT based super-resolution algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1672-1686
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• 2013

In this paper, we propose a hybrid super-resolution algorithm robust to cut-change. Existing single-frame based super-resolution algorithms are usually fast, but quantity of information for interpolation is limited. Although the existing multi-frame based super-resolution algorithms generally robust to this problem, the performance of algorithm strongly depends on motions of input video. Furthemore at boundary of cut, applying of the algorithm is limited. In the proposed method, we detect a define boundary of cut using cut-detection algorithm. Then we adaptively apply a single-frame based super-resolution method to detected cut. Additionally, we propose algorithms of normalizing motion vector and analyzing pattern of edge to solve various problems of existing super-resolution algorithms. The experimental results show that the proposed algorithm has better performance than other conventional interpolation methods.

• Journal of Digital Policy
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• v.3 no.1
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• pp.27-32
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• 2024

In this paper, proposes a super resolution method that enhances the quality of results by refining texture features, contrasting each, and utilizing the results as weights. For the improvement of quality, a precise and clear restoration result in details such as boundary areas is crucial in super resolution, along with minimizing unnecessary artifacts like noise. The proposed method constructs a residual block structure with multiple paths and skip-connections for feature estimation in conventional Convolutional Neural Network (CNN)-based super resolution methods to enhance quality. Additional learning is performed for sharpened and blurred image results for further texture analysis. By contrasting each super resolution result and allocating weights through this process, the proposed method achieves improved quality in detailed and smoothed areas of the image. The experimental results of the proposed method, evaluated using the PSNR and SSIM values as quality metrics, show higher results compared to existing algorithms, confirming the enhancement in quality.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.7-13
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• 2020

Meteorology and climate are closely related to human life. By using high-resolution weather data, services that are useful for real-life are available, and the need to produce high-resolution weather data is increasing. We propose a method for super-resolution temperature data using SRCNN. To evaluate the super-resolution temperature data, the temperature for a non-observation point is obtained by using the inverse distance weighting method, and the super-resolution temperature data using interpolation is compared with the super-resolution temperature data using SRCNN. We construct an SRCNN model suitable for super-resolution of temperature data and perform super-resolution of temperature data. As a result, the prediction performance of the super-resolution temperature data using SRCNN was about 10.8% higher than that using interpolation.