• Journal of Internet Computing and Services
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• v.19 no.4
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• pp.45-52
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• 2018

A single-image super-resolution is a process of restoring a high-resolution image from a low-resolution image. Recently, the super-resolution using the deep neural network has shown good results. In this paper, we propose a neural network structure that improves speed and performance over conventional neural network based super-resolution methods. To do this, we analyze the conventional neural network based super-resolution methods and propose solutions. The proposed method reduce the 5 stages of the conventional method to 3 stages. Then we have studied the optimal width and depth by experimenting on the width and depth of the network. Experimental results have shown that the proposed method improves the disadvantages of the conventional methods. The proposed neural network structure showed superior performance and speed than the conventional method.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.53-60
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• 2011

This paper proposes fusion methods of license plate detection and super-resolution for improving license plate recognition in low-resolution images. In the proposed method, we apply the license plate detection based on local structure pattern feature and the sequential super-resolution based on Kalman filter. The proposed fusion methods are divided into two according to whether the license plate is detected or not in the input image : (i) performing license plate detection after restoring whole image through super resolution, and (ii) restoring only the detected region through super-resolution after detecting the license plate. We demonstrated effectiveness of the proposed methods in various environments.

• 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 Broadcast Engineering
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• v.24 no.4
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• pp.633-642
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• 2019

Recently, the convolution neural network (CNN) model at a single image super-resolution (SISR) have been very successful. The residual learning method can improve training stability and network performance in CNN. In this paper, we propose a SISR using recursive residual network architecture by introducing dense skip connections for learning nonlinear mapping from low-resolution input image to high-resolution target image. The proposed SISR method adopts a method of the recursive residual learning to mitigate the difficulty of the deep network training and remove unnecessary modules for easier to optimize in CNN layers because of the concise and compact recursive network via dense skip connection method. The proposed method not only alleviates the vanishing-gradient problem of a very deep network, but also get the outstanding performance with low complexity of neural network, which allows the neural network to perform training, thereby exhibiting improved performance of SISR method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.5-8
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• 2019

단일 영상 초해상도 (Single Image Super-Resolution - SISR)기법은 카메라로 획득된 저해상도 영상에 필터 기반의 연산을 적용하여 좋은 화질의 고해상도 영상을 복원하는 과정이다. 최근에 심층 합성곱 신경망 학습의 발전에 따라 단일 영상 초해상도에 적용되는 심층 학습 기법들은 좋은 성과를 보여 주고 있다. 본 논문은 단일 영상 초해상도 성능을 개선하기 위해 웨이블릿 예측 네트워크를 효율적으로 적용하는 방법에 대해 연구하였으며, 저해상도 입력 영상의 특징을 잘 추출해내기 위해 네트워크 내부에 RDB를 적용하여 기존 방식보다 효율적으로 고해상도 영상 복원하는 기법을 제안한다. 모의실험을 통해 제안하는 방법이 기존 방법보다 화질은 약 PSNR 0.18dB만큼 우수하며 속도는 1.17배 빠른 것을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.117-118
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• 2016

최근 UHDTV(ultra high definition television)가 가정에 보급이 많이 되고 있는 추세지만, UHD급 콘텐츠가 매우 부족한 실정이다. 따라서 저해상도 FHD(full high definition) 영상을 고해상도 영상으로 변환시켜 재활용할 수 있는 초해상화(super-resolution, SR) 기술의 필요성이 커졌다. 그 중, 다층의 레이어로 구성된 다층 선형 매핑(multi-layer linear mappings, MLLM)을 기반으로 하는 제안된 초해상화 기법은 상대적으로 낮은 복잡도로 좋은 품질의 고해상도 영상을 복원할 수 있었다. 최근에는 강화 예측법을 추가하여 복원된 고해상도 영상의 품질을 더 향상시키는 기법이 등장하였는데, 이를 바탕으로 본 논문에서는 제안했었던 MLLM 기법을 위한 강화 예측법 기법을 새롭게 제안한다. 제안하는 초해상화 기법은 기존 MLLM 기법과 딥러닝 기반 초해상화 기법보다 높은 품질의 고해상도 영상을 생성하는 것을 확인하였다.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.345-354
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• 2005

Super resolution image reconstruction method refers to image processing algorithms that produce a high resolution(HR) image from observed several low resolution(LR) images of the same scene. This method has proven to be useful in many practical cases where multiple frames of the same scene can be obtained, such as satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. In this paper, we applied the super resolution reconstruction method in spatial domain to video sequences. Test images are adjacently sampled images from continuous video sequences and are overlapped at high rate. We constructed the observation model between the HR images and LR images applied with the Maximum A Posteriori(MAP) reconstruction method which is one of the major methods in the super resolution grid construction. Based on the MAP method, we reconstructed high resolution images from low resolution images and compared the results with those from other known interpolation methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.337-339
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• 2020

오디오 초 해상도 기술은 저 해상도의 오디오 신호를 이용하여 고 해상도의 오디오를 복원 또는 생성해 내는 기술이다. 본 기술 분야는 기존에 주파수 대역 확장, 인공 대역 확장 기술 등으로 연구되었으나, 최근 딥러닝 기술의 발전, 이미지 초 해상도 기술 연구 등에 힘입어 오디오 초 해상도 기술 이라는 이름으로 주로 연구되고 있다. 본 논문에서는 이러한 오디오 초 해상도 기술에 연구 동향에 대하여 설명하고, 기존의 논문 들에서 주로 다루고 있는 음성 데이터 베이스가 아닌 MedleyDB 음악 데이터 베이스를 활용하여 실험을 수행하였다. 실험은 4-폴드 교차 검증을 통해 수행되었으며, 실험 결과 제안하는 컨벌루션 신경망 구조 기반 오디오 초 해상도 기술은 입력 저해상도 오디오 대비 SNR 이 3.41 dB 향상됨을 확인하였다.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.121-129
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• 2021

This paper deals with a super-resolution that improves the resolution of side scan sonar images using learning-based compressive sensing. Learning-based compressive sensing combined with deep learning and compressive sensing takes a structure of a feed-forward network and parameters are set automatically through learning. In particular, we propose a method that can effectively extract additional information required in the super-resolution process through various initialization methods. Representative experimental results show that the proposed method provides improved performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) than conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.12-20
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• 2018

Efforts have been made to reconstruct low-resolution underwater images to high-resolution ones by using the image SR (Super-Resolution) method, all to improve efficiency when acquiring side-scan sonar images. As side-scan sonar images are similar with the optical images with respect to exploiting 2-dimensional signals, conventional image restoration methods for optical images can be considered as a solution. One of the most typical super-resolution methods for optical image is a sparse coding and there are studies for verifying applicability of sparse coding method for underwater images by analyzing sparsity of underwater images. Sparse coding is a method that obtains recovered signal from input signal by linear combination of dictionary and sparse coefficients. However, it requires huge computational load to accurately estimate sparse coefficients. In this study, a sparse coding based underwater image super-resolution method is applied while a selective reconstruction method for object region is suggested to reduce the processing time. For this method, this paper proposes an edge detection and object and non object region classification method for underwater images and combine it with sparse coding based image super-resolution method. Effectiveness of the proposed method is verified by reducing the processing time for image reconstruction over 32 % while preserving same level of PSNR (Peak Signal-to-Noise Ratio) compared with conventional method.