• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.120-124
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• 2017

The temporal super resolution is a method for increasing the frame rate. Electro-optical sensors are used in various surveillance and reconnaissance weapons systems, and the spatial resolution and temporal resolution of the required electro-optical sensors vary according to the performance requirement of each weapon system. Because most image sensors capture images at 30~60 frames/second, it is necessary to increase the frame rate when the target moves and changes rapidly. This paper proposes a method to increase the frame rate using color channel extrapolation. Using a DMD, one frame of a general camera was adjusted to have different consecutive exposure times for each channel, and the captured image was converted to a single channel image with an increased frame rate. Using the optical flow method, a virtual channel image was generated for each channel, and a single channel image with an increased frame rate was converted to a color channel image. The performance of the proposed temporal super resolution method was confirmed by the simulation.

• 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.

• 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.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.737-743
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• 2014

For single image super resolution (SR), interpolation based and example based algorithms are extensively used. The interpolation algorithms have the strength of theoretical simplicity. However, those algorithms are tending to produce high resolution images with jagged edges, because they are not able to use more priori information. Example based algorithms have been studied in the past few years. For example based SR, the nearest neighbor based algorithms are extensively considered. Among them, neighbor embedding (NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the sizes of local training sets are always too small. So, NE algorithm is weak in the performance of the visuality and quantitative measure by the poor generalization of nearest neighbor estimation. An improved NE algorithm with Support Vector Regression (SVR) was proposed to solve this problem. Given a low resolution image, the pixel values in its high resolution version are estimated by the improved NE. Comparing with bicubic and NE, the improvements of 1.25 dB and 2.33 dB are achieved in PSNR. Experimental results show that proposed method is quantitatively and visually more effective than prior works using bicubic interpolation and NE.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.19-26
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• 2020

In this paper, we propose a method to analyze the loss region of the dictionary-based super resolution result learned for image quality improvement and to map the learning data according to the analyzed loss region. In the conventional learned dictionary-based method, a result different from the feature configuration of the input image may be generated according to the learning image, and an unintended artifact may occur. The proposed method estimate loss information of low resolution images by analyzing the reconstructed contents to reduce inconsistent feature composition and unintended artifacts in the example-based super resolution process. By mapping the training data according to the final interpolation feature map, which improves the noise and pixel imbalance of the estimated loss information using a Gaussian-based kernel, it generates super resolution with improved noise, artifacts, and staircase compared to the existing super resolution. For the evaluation, the results of the existing super resolution generation algorithms and the proposed method are compared with the high-definition image, which is 4% better in the PSNR (Peak Signal to Noise Ratio) and 3% in the SSIM (Structural SIMilarity Index).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1477-1485
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• 2021

Recently, super-resolution has been intensively studied only on upscaling models with integer magnification. However, the need to expand arbitrary magnification is emerging in representative application fields of actual super-resolution, such as object recognition and display image quality improvement. In this paper, we propose a model that can support arbitrary magnification by using the weights of the existing integer magnification model. This model converts super-resolution results into the DCT spectral domain to expand the space for arbitrary magnification. To reduce the loss of high-frequency information in the image caused by the expansion by the DCT spectral domain, we propose a high-frequency attention network for arbitrary magnification so that this model can properly restore high-frequency spectral information. To recover high-frequency information properly, the proposed network utilizes channel attention layers. This layer can learn correlations between RGB channels, and it can deepen the model through residual structures.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.242-251
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• 2020

Recently, many deep convolutional neural networks for image super-resolution have been studied. Existing deep learning-based super-resolution algorithms are architecture that up-samples the resolution at the end of the network. The post-upsampling architecture has an inefficient structure at large scaling factor result of predicting a lot of information for mapping from low-resolution to high-resolution at once. In this paper, we propose a single image super-resolution using Channel Attention Residual Dense Block based on an iterative up-down sampling architecture. The proposed algorithm efficiently predicts the mapping relationship between low-resolution and high-resolution, and shows up to 0.14dB performance improvement and enhanced subjective image quality compared to the existing algorithm at large scaling factor result.

• Defense and Technology
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• no.2 s.180
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• pp.42-49
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• 1994

해상초계기 전술지원소의 기능은 해상초계기의 비행전, 비행중, 비행후 임무지원을 위한 전술전사와 통신 기능이다. 비행전 임무지원은 승무원 브리핑 및 임무수행중인 항공기 상황전시를, 비행중 임무지원은 임무항공기 상황전시 및 데이터링크 통신을, 비행후 임무지원은 임무재현, 음향 및 비음향 기록자료 분석을 포함한다.

• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.345-347
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• 2021

단일 이미지 초해상도는 딥러닝의 발전과 함께 놀라운 성능 향상이 이루어 졌다. 이러한 딥러닝 모델은 매우 많은 파라미터를 갖고 있어 많은 연산량과 메모리를 필요로 한다. 하지만 사용할 수 있는 리소스는 한정되어 있기 때문에 네트워크를 경량화 시키려는 연구도 지속되어 왔다. 본 논문에서는 Stochastic Weight Averaging (SWA) 알고리즘을 이용하여 상대적으로 적은 양의 메모리와 연산을 추가해 이미지 초해상도 모델의 성능을 높이고 안정적인 학습을 달성하였다. SWA 알고리즘을 적용한 모델은 그렇지 않은 모델에 비해 테스트셋에서 최대 0.13dB 의 성능 향상을 보였다.