• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.115-118
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• 2004

Images with high resolution are desired and often required in many visual applications. When resolution can not be improved by replacing sensors, either because of cost or hardware physical limits, super resolution image reconstruction method is what can be resorted to. Super resolution image reconstruction method refers to image processing algorithms that produce high quality and high resolution images from a set of low quality and low resolution images. The method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. The method can be either the frequency domain approach or the spatial domain approach. Much of the earlier works concentrated on the frequency domain formulation, but as more general degradation models were considered, later researches had been almost exclusively on spatial domain formulations. The method in spatial domains has three stages: i) motion estimate or image registration, ii) interpolation onto high resolution grid and iii) deblurring process. The super resolution grid construction in the second stage was discussed in this paper. We applied the Maximum A­Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from a set of low resolution images and compared the results with those from other known interpolation methods.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.71-76
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• 2020

In this paper, we propose a super-resolution method that reconstructs compressed low-resolution images into high-resolution images. We propose a CNN model with a small number of parameters, and even if quantization is applied to the proposed model, super-resolution can be implemented without deteriorating the image quality. To further improve the quality of the compressed low-resolution image, a new degradation model was proposed instead of the existing bicubic degradation model. The proposed degradation model is used only in the training process and can be applied by changing only the parameter values to the original CNN model. In the super-resolution image applying the proposed degradation model, visual artifacts caused by image compression were effectively removed. As a result, our proposed method generates higher PSNR values at compressed images and shows better visual quality, compared to conventional CNN-based SR methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.725-728
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• 1988

Image restoration technique using dual sensor is presented in this paper. Digital Radiography image (1024xlO24) is obtained by conventional resolution sensor. We also obtain local DR image data by high resolution sensor. Two dimensional maximum entropy power spectrum estimation (2-D ME PSE) is applied to low resolution image and high resolution image for the purpose of the power spectrum estimation of each image. A class of linear algebraic restoration filter, parametric projection filter (PPF), is derived from the power spectrums of each image. It is shown that the noise energy may be considerably reduced through the PPF.

• 대한원격탐사학회지
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• 제39권4호
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• pp.395-407
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• 2023

Super-resolution (SR) has great significance in image processing because it enables downstream vision tasks with high spatial resolution. Recently, SR studies have adopted deep learning networks and achieved remarkable SR performance compared to conventional example-based methods. Deep-learning-based SR models generally require low-resolution (LR) images and the corresponding high-resolution (HR) images as training dataset. Due to the difficulties in obtaining real-world LR-HR datasets, most SR models have used only HR images and generated LR images with predefined degradation such as bicubic downsampling. However, SR models trained on simple image degradation do not reflect the properties of the images and often result in deteriorated SR qualities when applied to real-world images. In this study, we propose an image degradation model for HR satellite images based on the modulation transfer function (MTF) of an imaging sensor. Because the proposed method determines the image degradation based on the sensor properties, it is more suitable for training SR models on remote sensing images. Experimental results on HR satellite image datasets demonstrated the effectiveness of applying MTF-based filters to construct a more realistic LR-HR training dataset.

• 대한전자공학회논문지SP
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• 제43권2호
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• pp.12-18
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• 2006

이 논문은 저해상도 동영상에서 자동화된 방식으로 한 장의 좋은 화질의 고해상도 영상을 얻는 방안을 제시한다. 여러 장의 저해상도 영상을 이용하여 고해상도 영상을 얻는 방법이 한 장의 저해상도 영상만을 이용하는 전통적인 보간 방법에 비해 좋은 결과를 보이기 위해서는 입력 영상들이 공통된 고해상도 격자에 잘 정합되어야 하므로, 정합오차를 충분히 고려하여 입력영상들을 주의 깊게 선택한다. 본 논문에서는 움직임 보상된 저해상도 영상들로부터 얻어진 통계적 특성을 활용하여 입력 영상 후보들의 입력 영상으로서의 적합성을 평가한다. 고해상도 영상획득모델로부터 움직임 보상오차의 최대값을 추정한다. 입력 영상 후보의 움직임 보상오차가 추정된 움직임 보상오차의 최대값보다 크면 입력 영상후보는 선정에서 제외된다. 선정된 적절한 유효 입력 영상 후보의 수와 움직임 보상오차의 통계치를 고려하여 최종 입력 영상들을 선별한다. 입력 영상 선별부에서 최종적으로 선별된 입력 영상들은 뒤따르는 고해상도 영상복원부로 입력된다. 제안된 방식은 사용자의 간섭없이 저해상도 동영상에서 효과적으로 입력 영상들을 선별하여 좋은 화질의 고해상도 영상을 얻는 응용에 사용될 것으로 기대된다.

• 한국측량학회지
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• 제30권6_2호
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• pp.653-663
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• 2012

Using soft classification, it is possible to obtain the land cover proportions from the remotely sensed image. These land cover proportions are then used as input data for a procedure called "super-resolution mapping" to produce the predicted hard land cover layers at higher resolution than the original remotely sensed image. Superresolution mapping can be implemented using a number of algorithms in which the Hopfield Neural Network (HNN) has showed some advantages. The HNN has improved the land cover classification through superresolution mapping greatly with the high resolution data. However, the super-resolution mapping is based on the spatial dependence assumption, therefore it is predicted that the accuracy of resulted land cover classes depends on the relative size of spatial features and the spatial resolution of the remotely sensed image. This research is to evaluate the capability of HNN to implement the super-resolution mapping for SPOT image to create higher resolution land cover classes with different zoom factor.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권7호
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• pp.573-577
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• 2010

영상 해상도 개선은 영상 복원이나 확대 같은 응용 분야에서 널리 사용되는 기술로서, 결과 영상에서의 블록 현상이나 인공물 발생과 같은 화질 열화를 제거하는 것이 중요하다. 본 논문에서는 영상의 손실 정보를 이용하는 영상 해상도 개선 방법을 제안한다. 제안하는 방법은 획득 저해상도를 하위 레벨 보간을 통해서 손실 정보를 계산 및 추정하고 이를 보간된 고해상도 영상에 적용함으로서 1차적인 보간을 수행하고 획득 저해상도 영상과의 에러를 계산한 후 다시 보간된 영상에 적용하는 과정을 반복하여 최종적인 보간 영상을 생성한다. 동일한 영상을 이용한 시험을 통해서 비교 방법들보다 평균 PSNR에서 3.2㏈ 이상 향상된 것을 확인하였고, 주관적 화질도 개선된 것을 알 수 있었다. 또한 계산복잡도를 85% 이상 감소시킬 수 있었다. 제안한 해상도 개선 방법은 영상 처리의 다양한 분야에서 기반 기술로 사용될 수 있다.

• 한국컴퓨터정보학회논문지
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• 제22권3호
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• pp.45-51
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• 2017

In this paper, we propose a consecutive-frame super-resolution method to tackle a moving object problem. The super-resolution is a method restoring a high resolution image from a low resolution image. The super-resolution is classified into two types, briefly, single-frame super-resolution and consecutive-frame super-resolution. Typically, the consecutive-frame super-resolution recovers a better than the single-frame super-resolution, because it use more information from consecutive frames. However, the consecutive-frame super-resolution failed to recover the moving object. Therefore, we proposed an improved method via moving object detection. Experimental results showed that the proposed method restored both the moving object and the background properly.

• 디지털콘텐츠학회 논문지
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• 제19권6호
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• pp.1197-1205
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• 2018

본 논문에서는 함정전투체계의 EOTS나 IRST에서 획득한 영상을 초고해상도 영상으로 복원한다. 저해상도에서 초고해상도의 영상을 생성하는 생성 모델과 이를 판별하는 판별 모델로 구성된 생성적 적대 신경망을 이용하고, 다양한 학습 파라미터의 변화를 통한 최적의 값을 제안한다. 실험에 사용되는 학습 파라미터는 crop size와 sub-pixel layer depth, 학습 이미지 종류로 구성되며, 평가는 일반적인 영상 품질 평가 지표에 추가적으로 특징점 추출 알고리즘을 함께 사용하였다. 그 결과, Crop size가 클수록, Sub-pixel layer depth가 깊을수록, 고해상도의 학습이미지를 사용할수록 더 좋은 품질의 영상을 생성한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.219-222
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• 2007

Today, use of high resolution satellite image with at least 1m resolution is expanding into many more areas including forest, river way, city, seashore and so forth for disaster prevention. Interest in this medium is increasing among the general public due to the roll-out to the private sector as Google earth, Virtual Earth and so forth. However, pre-processing process that revises the geometrical distortion that result at the time of photographing is required in order to use high resolution satellite image. The purpose of this research is to search the most accurate GCP(Ground Control Point) information acquisition method that is used for the revision of high resolution satellite image's geometrical distortion through automated processing. Through this, it is possible to contribute to increasing the level of accuracy at the time of high resolution satellite image revision and to secure promptness.