• 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 the Korean Society of Radiology
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• v.82 no.3
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• pp.654-669
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• 2021

Purpose To evaluate the accuracy of MRI in predicting the pathological complete response (pCR) and the residual tumor size of breast cancer after neoadjucant chemotherapy (NAC), and to determine the factors affecting the accuarcy. Materials and Methods Eighty-eight breast cancer patients who underwent surgery after NAC at our center between 2010 and 2017 were included in this study. pCR was defined as the absence of invasive cancer on pathological evaluation. The maximum diameter of the residual tumor on post-NAC MRI was compared with the tumor size of the surgical specimen measured pathologically. Statistical analysis was performed to elucidate the factors affecting pCR and the residual tumor size-discrepancy between the MRI and the pathological measurements. Results The pCR rate was 10%. The diagnostic accuracy of MRI and the area under the curve for predicting pCR were 90.91% and 0.8017, respectively. The residual tumor sizes obtained using MRI and pathological measurements showed a strong correlation (r = 0.9, p < 0.001), especially in patients with a single mass lesion (p = 0.047). The size discrepancy between MRI and the pathological measurements was significantly greater in patients with the luminal type (p = 0.023) and multifocal tumors/non-mass enhancement on pre-NAC MRI (p = 0.047). Conclusion MRI is an accurate tool for evaluating pCR and residual tumor size in breast cancer patients who receive NAC. Tumor subtype and initial MRI features affect the accuracy of MRI.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.119-121
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• 2019

부가적 백색 잡음 모델(additive white Gaussian noise, AWGN에서 학습된 깊은 신경만 (deep neural networks)을 이용한 잡음 제거기는 제거하려는 잡음이 AWGN인 경우에는 뛰어난 성능을 보이지만 실제 카메라 잡음에 대해서 잡음 제거를 시도하였을 때는 성능이 크게 저하된다. 본 논문은 U-Net 구조의 깊은 인공신경망 모델에 residual block을 결합함으로서 실제 카메라 영상에서 기존 알고리즘보다 뛰어난 성능을 지니는 신경망을 제안하다. 제안한 방법을 통해 Darmstadt Noise Dataset에서 PSNR과 SSIM 모두 CBDNet 대비 향상됨을 확인하였다.

• Women's Health Nursing
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• v.16 no.4
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• pp.409-418
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• 2010

Purpose: The purpose of this study was to identify the effects of a pre-operation video assisted education program on residual urine, gas passing and state anxiety in women undergoing hysterectomy. Methods: Nonequivalent control group non-synchronized design was used for the study. In the research, video assisted education program was applied to the experimental group while a similar conventional education was done to the control group. The pre-operation state anxiety and post-operation residual urine and gas discharge of both the groups were measured. The data were analyzed using SPSS. Results: The experimental group was significantly higher than control group on gas passing (t=3.04, p=.00). However the residual urine (t=0.34, p=.73) and state anxiety (t=0.81, p=.82) did not make significant differences. Conclusion: This study is very meaningful in that it developed and provided a nursing intervention can positively affect hysterectomy patients. The pre-operation video assisted education program may be an effective nursing intervention that is clinically practical and useful to reduce time of the gas passing of hysterectomy patients after the operation.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.2
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• pp.37-44
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• 2020

In this paper, we proposed that a fully automatic multi-class whole heart segmentation algorithm using deep learning. The proposed method is based on U-Net architecture which consist of recurrent convolutional block, residual multi-dilated convolutional block. The evaluation was accomplished by comparing automated analysis results of the test dataset to the manual assessment. We obtained the average DSC of 96.88%, precision of 95.60%, and recall of 97.00% with CT images. We were able to observe and analyze after visualizing segmented images using three-dimensional volume rendering method. Our experiment results show that proposed method effectively performed to segment in various heart structures. We expected that our method can help doctors and radiologist to make image reading and clinical decision.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.124-131
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• 2023

Object detection techniques are increasingly used to obtain information on physical characteristics or situations of a specific area from remote images. The accuracy of object detection is decreased in remote sensing images with low resolution because the low resolution reduces the amount of detail that can be captured in an image. A single neural network is proposed to joint the super-resolution method and object detection method. The proposed method constructs a deep residual-based network to restore object features in low-resolution images. Moreover, the proposed method is used to improve the performance of object detection by jointing a single network with YOLOv5. The proposed method is experimentally tested using VEDAI data for low-resolution images. The results show that vehicle detection performance improved by 81.38% on mAP@0.5 for VISIBLE data.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.251-258
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• 2021

In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.79-84
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• 2015

In a distribution of digital image, there is a serious problem that is a distribution of the altered image by a forger. For the problem solution, this paper proposes a median filtering (MF) image forensic decision algorithm using a feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value' gradients of original image then 1th~6th order coefficients to be six feature vector. And the reconstructed image is produced by the solution of Poisson's equation with the gradients. From the difference image between original and its reconstructed image, four feature vector (Average value, Max. value and the coordinate i,j of Max. value) is extracted. Subsequently, Two kinds of the feature vector combined to 10 Dim. feature vector that is used in the learning of a SVM (Support Vector Machine) classification for MF (Median Filtering) detector of the altered image. On the proposed algorithm of the median filtering detection, compare to MFR (Median Filter Residual) scheme that had the same 10 Dim. feature vectors, the performance is excellent at Unaltered, Averaging filtering ($3{\times}3$) and JPEG (QF=90) images, and less at Gaussian filtering ($3{\times}3$) image. However, in the measured performances of all items, AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.782-785
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• 2015

In this paper, we propose a novel residual differential pulse-code modulation (RDPCM) coding technique to improve coding efficiency of screen content videos. The proposed method uses a weighted combination of adjacent residues to provide an accurate estimate in RDPCM. The weights are trained in previously coded samples by using an L1 optimization problem with the least absolute shrinkage and selection operation (LASSO). The proposed method achieves BD-rate saving about 3.1% in all-intra coding.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1704-1712
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• 1999

This research features efficient detection of obstacles, especially vehicles, in the forward direction of navigation for the development of unmanned automous vehicle. We separate image regions into ground and non-ground planes using the Helmholtz shearing technique in order to reliably exclude regions that do not contain obstacles. We propose a computationally simple and efficient method for the detection of vehicles in the forward direction by analysis of horizontally and vertically projected histograms of residual disparity map obtained from Helmholtz shearing. We have experimented the proposed method on real outdoor stereo data. Experimental results show that our method gives accurate detection of forward vehicles and is computationally very efficient.