• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1604-1611
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• 2020

Light field image captured by a microlens array-based camera has many limitations in practical use due to its low spatial resolution and angular resolution. High spatial resolution images can be easily acquired with a single image super-resolution technique that has been studied a lot recently. But there is a problem in that high angular resolution images are distorted in the process of using disparity information inherent among images, and thus it is difficult to obtain a high-quality angular resolution image. In this paper, we propose light field angular super-resolution that extracts an initial feature map using an dilated convolutional neural network in order to effectively extract the view difference information inherent among images and generates target image using a residual neural network. The proposed network showed superior performance in PSNR and subjective image quality compared to existing angular super-resolution networks.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1271-1278
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• 2018

The NDT using ultrasonic is largely divided into A-Scan and C-Scan methods. Since A-Scan method is subject to subjective judgement by trained personnel, C-Scan method has been introduced, which presents the weld area in two dimensions by placing the transducers two dimensionally used in the A-Scan method. Therefore, it is necessary to develop equipment that can provide weld quality without the help of a welding expert and the presentation of effective C-Scan images. Thus, in this paper, the algorithms that express a low resolution 2-dimensional gray image formed by C-Scan method as a high-resolution color C-Scan image and automatically determine the weld quality from the generated C-Scan color image. The high resolution color C-Scan images proposed in this paper allow the exact shape of the weld point to be expressed, and an objective algorithm to use this image to automatically determine weld quality.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1402-1404
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• 1987

Photoconductive target was fabricated to make vidicon available. In order for a vidicon to operate well, it is essential that the target have high photosensitivity, low image lag, and high resolution. In the vidicon mode analysis, photosensitivity of 0.8, image lag of 30%, resolution of 300 TV lines, and the S/N ratio of 30 dB at 10 lux illumination were measured.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4065-4083
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• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4326-4344
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• 2021

The SinGAN is one of generative adversarial networks that can be trained on a single nature image. It has poor ability to learn more global features from nature image, and losses much local detail information when it generates arbitrary size image sample. To solve the problem, a non-linear function is firstly proposed to control downsampling ratio that is ratio between the size of current image and the size of next downsampled image, to increase the ratio with increase of the number of downsampling. This makes the low-resolution images obtained by downsampling have higher proportion in all downsampled images. The low-resolution images usually contain much global information. Therefore, it can help the model to learn more global feature information from downsampled images. Secondly, the attention mechanism is introduced to the generative network to increase the weight of effective image information. This can make the network learn more local details. Besides, in order to make the output image more natural, the TVLoss function is introduced to the loss function of SinGAN, to reduce the difference between adjacent pixels and smear phenomenon for the output image. A large number of experimental results show that our proposed model has better performance than other methods in generating random samples with fixed size and arbitrary size, image harmonization and editing.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.6
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• pp.67-74
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• 2006

In this paper, a tiled display system is developed to get a high-resolution image in visualization of large-scale structural analysis data with low-resolution display devices and low-cost cluster computer system. Concerning the hardware system, some of the crucial points are investigated, and a new beam-projector positioner is designed and manufactured to resolve the keystone phenomena which result in distorted image. In the development of tiled display software, Qt and OpenGL are utilized for GUI and rendering, respectively. To obtain the entire tiled image, LAM-MPI is utilized to synchronize the several sub-images produced from each cluster computer node.

• 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 Information Display
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• v.8 no.4
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• pp.27-31
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• 2007

In this paper, we present an efficient tiled stereo display system for tangible meeting. For tangible meeting, it is important to provide immersive display with high resolution image to cover up the field of view and provide to the local user the same environment as that of remote site. To achieve these, a high resolution image needs to be transmitted for reconstruction of remote world, and it should be displayed using a tiled display. However, it is hard to transmit high resolution image in real time due to the limit of network bandwidth, and so we receive multiple images and reconstruct a remote world with received images in advance. Then, we update only a specific area where remote user exists by receiving low resolution image in realtime. We synthesize the transmitted image to the existing environmental map of remote world and display it as a stereo image. For this, we developed a new system which supports GPU based real time warping and blending, automatic feature extraction using machine vision technique.

• Journal of International Society for Simulation Surgery
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• v.1 no.2
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• pp.57-61
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• 2014

We present a robust 3D facial reconstruction method using a single image generated by face-specific super resolution technique. Based on the several consecutive frames with low resolution, we generate a single high resolution image and a three dimensional facial model based on it. To do this, we apply PME method to compute patch similarities for SR after two-phase warping according to facial attributes. Based on the SRI, we extract facial features automatically and reconstruct 3D facial model with basis which selected adaptively according to facial statistical data less than a few seconds. Thereby, we can provide the facial image of various points of view which cannot be given by a single point of view of a camera.