• Journal of the Korean Society of Radiology
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• v.17 no.2
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• pp.191-199
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• 2023

This study aimed to propose minimized radiation doses with an optimized abdomen x-ray image, which realizes a Deep Blind Image Super-Resolution Generative adversarial network (BSRGAN) technique. Entrance surface doses (ESD) measured were collected by changing exposure conditions. In the identical exposures, abdominal images were acquired and were processed with the BSRGAN. The images reconstructed by the BSRGAN were compared to a reference image with 80 kVp and 320 mA, which was evaluated by mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). In addition, signal profile analysis was employed to validate the effect of the images reconstructed by the BSRGAN. The exposure conditions with the lowest MSE (about 0.285) were shown in 90 kVp, 125 mA and 100 kVp, 100 mA, which decreased the ESD in about 52 to 53% reduction), exhibiting PSNR = 37.694 and SSIM = 0.999. The signal intensity variations in the optimized conditions rather decreased than that of the reference image. This means that the optimized exposure conditions would obtain reasonable image quality with a substantial decrease of the radiation dose, indicating it could sufficiently reflect the concept of As Low As Reasonably Achievable (ALARA) as the principle of radiation protection.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.154-161
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• 2015

A novel multiframe super-resolution (SR) algorithm is presented to overcome the limitation of existing single-image SR algorithms using motion information from adjacent frames in a video. The proposed SR algorithm consists of three steps: i) definition of a local region using interframe motion vectors, ii) multiscale patch generation and adaptive selection of multiple optimum patches, and iii) combination of optimum patches for super-resolution. The proposed algorithm increases the accuracy of patch selection using motion information and multiscale patches. Experimental results show that the proposed algorithm performs better than existing patch-based SR algorithms in the sense of both subjective and objective measures including the peak signal-to-noise ratio (PSNR) and structural similarity measure (SSIM).

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.7-14
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• 2018

Salient information extraction from multi-view videos is a very challenging area because of inter-view, intra-view correlations, and computational complexity. There are several techniques developed for keyframes extraction from multi-view videos with very high computational complexities. In this paper, we present a keyframes extraction approach from multi-view videos using entropy and complexity information present inside frame. In first step, we extract representative shots of the whole video from each view based on structural similarity index measurement (SSIM) difference value between frames. In second step, entropy and complexity scores for all frames of shots in different views are computed. Finally, the frames with highest entropy and complexity scores are considered as keyframes. The proposed system is subjectively evaluated on available office benchmark dataset and the results are convenient in terms of accuracy and time complexity.

• Korean Journal of Optics and Photonics
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• v.34 no.5
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• pp.192-201
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• 2023

The band-limited angular spectrum method (BL-ASM) causes aliasing errors due to spatial frequency control problems. In this paper, a sampling interval adjustment technique for phase holograms and a technique for reducing speckle noise and improving image quality using a deep-learningbased U-net model are proposed. With the proposed technique, speckle noise is reduced by first calculating the sampling factor and controlling the spatial frequency by adjusting the sampling interval so that aliasing errors can be removed in a wide range of propagation. The next step is to improve the quality of the reconstructed image by learning the phase hologram to which the deep learning model is applied. In the S/W simulation of various sample images, it was confirmed that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were improved by 5% and 0.14% on average, compared with the existing BL-ASM.

• Journal of the Korean Society of Radiology
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• v.17 no.2
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• pp.215-221
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• 2023

Among brain CT scan conditions including the lens, the tube voltage was changed to 80, 100, and 120 kVp and applied. The change in dose was analyzed using lead, lead goggles and barium sulfate silicon shielding materials, and the degree of influence of the shielding materials on image quality was compared and analyzed by applying the SNR, CNR, and SSIM index analysis methods. As a result, it was analyzed that although the dose was reduced by applying all shielding materials, the difference in dose reduction was not large (P > 0.05). In addition, as for the change in image quality due to the application of the shielding material, SNR and CNR were the highest when lead goggles were applied, and the structural similarity was measured to be the best as it was closest to the reference value of 1 in SSIM analysis. Therefore, based on the results of this study, it is thought that if more diverse shielding materials and clinical test results are derived and applied, it will be helpful for the clinical application criteria in the case of shielding utilization inspection.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.6
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• pp.735-744
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• 2019

One of the key factors for recognition performance in the automatic target recognition for synthetic aperture radar imagery(SAR-ATR) system is reliability of the SAR target database. To achieve optimal performance, the database should be constructed using the images obtained under the same operating condition as the SAR sensor. However, it is impractical to have the extensive set of real-world SAR images, and thus those from the electro magnetic prediction tool with 3-D CAD models are suggested as an alternative where their reliability can be always questionable. In this paper, a method for similarity assessment between target SAR images is presented inspired by the fact that a target SAR image is mainly characterized by the features of scattering centers. The method is demonstrated using a variety of examples and quantitatively measures the similarity related to reliability. Its assessment performance is further compared with that of the existing metric, structural similarity(SSIM).

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.212-215
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• 2021

본 논문에서는 6 Fisheye lens 원본 영상에 대하여 Insta360 stitcher, AutoStitch[4], As-Projective-AsPossible(APAP)[5] 스티칭 방법으로 360 도 파노라마 영상을 생성하고 기하학적 왜곡과 컬러 왜곡을 비교 평가한다. 360 도 파노라마 Image Quality Assessment(IQA) 메트릭으로 Natural Image Quality Evaluator(NIQE)[6], Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE)[7], Perception based Image Quality Evaluator(PIQE)[8], Feature Similarity(FSIM)[9] 그리고 high frequency feature 에 대한 Structural Similarity(SSIM)[10]을 측정하여 정량적 평가를 하며 정성적인 비교를 통하여 파노라마 영상의 품질과 평가 메트릭에 대한 벤치마크를 제공한다.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.41-47
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• 2019

In this paper, we propose a application of conditional generative adversarial network (cGAN) for generation of contrast enhanced computed tomography (CT) image. Two types of CT data which were the enhanced and non-enhanced were used and applied by the histogram equalization for adjusting image intensities. In order to validate the generation of contrast enhanced CT data, the structural similarity index measurement (SSIM) was performed. Prepared generated contrast CT data were analyzed the statistical analysis using paired sample t-test. In order to apply the optimized algorithm for the lymph node cancer, they were calculated by short to long axis ratio (S/L) method. In the case of the model trained with CT data and their histogram equalized SSIM were $0.905{\pm}0.048$ and $0.908{\pm}0.047$. The tumor S/L of generated contrast enhanced CT data were validated similar to the ground truth when they were compared to scanned contrast enhanced CT data. It is expected that advantages of Generated contrast enhanced CT data based on deep learning are a cost-effective and less radiation exposure as well as further anatomical information with non-enhanced CT data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.518-523
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• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.728-735
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• 2017

With the proliferation of digital devices, the devices have generated considerable additive white Gaussian noise while acquiring digital images. The most well-known denoising methods focused on eliminating the noise, so detailed components that include image information were removed proportionally while eliminating the image noise. The proposed algorithm provides a method that preserves the details and effectively removes the noise. In this proposed method, the goal is to separate meaningful detail information in image noise environment using the edge strength and edge connectivity. Consequently, even as the noise level increases, it shows denoising results better than the other benchmark methods because proposed method extracts the connected detail component information. In addition, the proposed method effectively eliminated the noise for various noise levels; compared to the benchmark algorithms, the proposed algorithm shows a highly structural similarity index(SSIM) value and peak signal-to-noise ratio(PSNR) value, respectively. As shown the result of high SSIMs, it was confirmed that the SSIMs of the denoising results includes a human visual system(HVS).