• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.38-43
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• 2017

Many studies about generating images of seabed using active sonar have been carried out but image resolution enhancement is still an important problem. Many methods have been proposed to improve sonar resolution and the approach using narrow beam width is commonly and widely applied to enhance azimuth resolution. Unfortunately, this has technical limitations to reduce the beam width. Therefore, signal processing techniques are essential to achieving higher azimuth resolution when an array with conventional beam width is employed. This paper proposes a new approach that utilizes overlapped beams to obtain higher resolution.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.389-395
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• 2007

The Film X-ray and the CCD method of current medical image system have the disadvantages such as required large place and diagnosis time. In this paper, we implement an effective medical image system using TXT-DXD method's digital X-ray detector(DR1000C). The implemented medical image system has advantages of placing efficiency and short diagnosis time. In order to make the image out of the system more effective, we develop an LCD(Liquid Crystal Display) control driver, having the resolution of 1900*1200. And we propose an enhancement unsharp masking method to update image enhancement of DR1000C medical image system, and compare it with the current methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2068-2082
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• 2023

With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multistage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.2
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• pp.103-113
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• 1991

The accuracy of input coordinates of ground control points and check points affects great influences to the results of ground coordinate computation in using SPOT digital image data. The original SPOT images displayed on CRT are not usually adequate for identifying the object features and determining the point positioning. Hence, appropriate image processing techniques such as contrast enhancement, subpixel interpolation, edge enhancement, and spatial filtering are needed. In this study, the principles of digital image processing needed for accurate three dimensional positioning and spectral characteristic analysis are investigated. The algorithms for the actual applications are developed and programmed. And using the developed image processing software, some SPOT P-mode and XS-mode images are merged into the SPOT P＋XS, the high-resolution color composite image.

• The Journal of Information Technology
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• v.5 no.2
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• pp.69-80
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• 2002

Conventionally, we have used an acoustic microscope at single operating frequency. The resolution and quality of the measured images are determined by transducer of the microscope. In this paper, we have studied Vertical Resolution Enhancement with Acoustic Reflection Microscope using combining bases of support for SFR(Spatial Frequency Response). Increased Vertical resolution can be obtained by taking three-dimensional images at more that one frequency and numerically combining the results. As results of the experiment, we could get enhanced images with the rate of contrast in proportion to the changing rate of depth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2882-2903
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• 2022

Image super-resolution (SR) processing is of great value in the fields of digital image processing, intelligent security, film and television production and so on. This paper proposed a densely connected deep learning network based on cascade architecture, which can be used to solve the problem of super-resolution in the field of image quality enhancement. We proposed a more efficient residual scaling dense block (RSDB) and the multi-channel cascade architecture to realize more efficient feature reuse. Also we proposed a hybrid loss function based on L₁ error and L_∞ error to achieve better L_∞ error performance. The experimental results show that the overall performance of the network is effectively improved on cascade architecture and residual scaling. Compared with the residual dense net (RDN), the PSNR / SSIM of the new method is improved by 2.24% / 1.44% respectively, and the L_∞ performance is improved by 3.64%. It shows that the cascade connection and residual scaling method can effectively realize feature reuse, improving the residual convergence speed and learning efficiency of our network. The L_∞ performance is improved by 11.09% with only a minimal loses of 1.14% / 0.60% on PSNR / SSIM performance after adopting the new loss function. That is to say, the L_∞ performance can be improved greatly on the new loss function with a minor loss of PSNR / SSIM performance, which is of great value in L_∞ error sensitive tasks.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.408-417
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• 2003

In this paper, we have studied the enhancement of the acoustic image by combining bases of support for SFR (Spatial Frequency Response) taken at multi-frequencies. The scanning acoustic microscope system have been constructed using the quadrature detector that is able to measure the amplitude and phase of the reflected signal simultaneously. Both real and quadrature components of reflected signal have been acquired at 4.4 ㎒ to 5.6 ㎒ reliably and accurately. In this experimental result, better depth resolution can be obtained by numerically combining images taken at several different frequencies. Image intensity have been better about 3.4 times at multi-frequency than one at a single frequency.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.3
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• pp.234-245
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• 2023

The Arena Fragmentation Test(AFT) is designed to analyze warhead performance by measuring fragmentation data. In order to evaluate the results of the AFT, a set of AFT images are captured by high-speed cameras. To detect objects in the AFT image set, ResNet-50 based Faster R-CNN is used as a detection model. However, because of the low resolution of the AFT image set, a detection model has shown low performance. To enhance the performance of the detection model, Super-resolution(SR) methods are used to increase the AFT image set resolution. To this end, The Bicubic method and three SR models: ZSSR, EDSR, and SwinIR are used. The use of SR images results in an increase in the performance of the detection model. While the increase in the number of pixels representing a fragment flame in the AFT images improves the Recall performance of the detection model, the number of pixels representing noise also increases, leading to a slight decreases in Precision performance. Consequently, the F1 score is increased by up to 9 %, demonstrating the effectiveness of SR in enhancing the performance of the detection model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.687-692
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• 2018

In this paper, the development of an automotive thermal imaging camera providing image information for ADAS (Advanced Driver Assist System) and autonomous vehicles is described and an improved technique to enhance the details of the image is proposed. Thermal imaging cameras are used in various fields, such as the medical, industrial and military fields, for the purpose of temperature measurement and night vision. In automobiles, they are utilized for night vision systems. For their utilization in ADAS and autonomous vehicles, appropriate image resolution and enhanced detail are required for object recognition. In this study, a $640{\times}480$ resolution thermal imaging camera that can be applied to automobiles is developed and the BDE (Block-Range Detail Enhancement) technique is applied to improve the details of the image. In order to improve the image detail obtained in various driving environments, the block-range values between the target pixel and the surrounding 8 pixels are calculated and classified into 5 levels. Then, different factors are added or subtracted to obtain images with high utilization. The improved technique distinguishes the dark part of the image by the resulting temperature difference of 130mK and shows an improvement in the fine detail in both the bright and dark parts of the image. The developed thermal imaging camera using the improved detail enhancement technique is applied to a test vehicle and the results are presented.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.118-128
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• 2015

Compressive sensing (CS) has to face with two challenges of computational complexity reconstruction and low coding efficiency. As a solution, this paper presents a novel spatially scalable Kronecker two layer compressive sensing framework which facilitates reconstruction up to three spatial resolutions as well as much improved CS coding performance. We propose a dual-resolution sensing matrix based on the quincunx sampling grid which is applied to the base layer. This sensing matrix can provide a fast-preview of low resolution image at encoder side which is utilized for predictive coding. The enhancement layer is encoded as the residual measurement between the acquired measurement and predicted measurement data. The low resolution reconstruction is obtained from the base layer only while the high resolution image is jointly reconstructed using both two layers. Experimental results validate that the proposed scheme outperforms both conventional single layer and previous multi-resolution schemes especially at high bitrate like 2.0 bpp by 5.75dB and 5.05dB PSNR gain on average, respectively.