• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.334-339
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• 2003

위성 영상의 활용범위가 확대되면서 다양한 위성 센서로부터 위성영상이 제공되고 있다. 특히 최근에는 이기종 센서로부터 서로 다른 시간과 분광정보를 가진 영상의 자동 등록이 영상자료 분석을 위해 필요한 기술로 인식되고 있다. 본 연구에서는 Kompsat 영상과 Radarsat 영상을 이용하여 두 영상에서 공통으로 존재하는 패치(Patch)를 추출하고 그 패치의 중심점을 찾아 매칭하는 방법에 기초를 둔 자동영상 등록 기법을 제시하였다. 밝기 값분석을 통해 패치를 추출하고 추출된 패치를 모폴로지(Morphology)기법과 잡음요소 제거 기법을 적용하여 패치에 포함된 잡음을 제거하였으며, 비용함수를 이용한 패치 매칭과 변환함수를 이용하여 자동영상등록을 실시하였다.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.1-9
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• 2014

Image inpainting is the widely used technique to restore a damaged region or to fill a hole in an image. The exemplar-based technique effectively generates new texture by copying colour values of the most correlated patch in the source into the empty region of the current patch. In traditional exemplar-based synthesis, the patch correlation is computed using only the already filled pixels of the current patch. Thus, by ignoring the correlation between the hole regions of the two patches, an undesirable patch which is highly correlated with the current patch in the already filled area but considerably dissimilar in the area to be filled can be selected, which results in bad texture propagation. To avoid such problems, a new exemplar-based inpainting method using patch extrapolation is proposed. The empty part of the current patch is extrapolated beforehand, and then the complete patch is used for finding its exemplar. Experimental results show that the proposed method provides more natural synthesis results than the conventional ones.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1317-1322
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• 2021

Nonlocal similarity of natural images leads to the fact that a patch matrix whose columns are similar patches of the reference patch has a low rank. Images corrupted by additive white Gaussian noises (AWGN) make their patch matrices to have a higher rank. The noise in the image can be reduced by obtaining low rank approximation of the patch matrices. In this paper, an image denoising algorithm is proposed, which first constructs the patch matrices by combining the similar patches of each reference patch, which is a part of the noisy image. For each patch matrix, the proposed algorithm calculates its low rank approximate, and then recovers the original image by aggregating the low rank estimates. The simulation results using widely accepted test images show that the proposed denoising algorithm outperforms four recent methods.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.264-272
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• 2010

Example-based super resolution is a method to restore a high resolution image from low resolution images through training and retrieval of image patches. It is not only good in its performance but also available for a single frame low-resolution image. However, its time complexity is very high because it requires lots of comparisons to retrieve image patches in restoration process. In order to improve the restoration speed, an efficient patch retrieval algorithm is essential. In this paper, we applied various high-dimensional feature retrieval methods, available for the patch retrieval, to a practical example-based super resolution system and compared their speed. As well, we propose to apply the multi-phase candidate reduction approach to the patch retrieval process, which was successfully applied in character recognition fields but not used for the super resolution. In the experiments, LSH was the fastest among conventional methods. The multi-phase candidate reduction method, proposed in this paper, was even faster than LSH: For $1024{\times}1024$ images, it was 3.12 times faster than LSH.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.833-836
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• 2004

세그먼트 패치(Segment Patch) 기반의 스테레오 매칭에 있어서, 매칭의 속도와 정확도는 세그먼트 패치를 생성하는 과정에 의존한다. 본 논문에서는 매칭 프리미티브로 사용되는 세그먼트 패치를 결정하는 과정으로 영상의 강도와 함께 인접 세그먼트 패치들 사이의 깊이를 고려하여 최적의 세그먼트 패치를 결정하는 방법을 제안하였다. 그 결과 영상의 강도 변화가 작은 영역에서 뿐만 아니라 패치 매칭의 취약함으로 지적되었던 복잡한 영역에서도 좋은 결과를 보여 주었다.

• 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).

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.279-279
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• 2019

본 연구는 안동 하천실증연구센터에서 수행된 RIED 실험의 일환으로 실험수로내 식재된 버드나무 식생패치의 밀도에 따른 표면흐름의 특성을 검토하는 실험이다. 실험의 목적은 식생의 침수수위에 따른 식생주변 및 하도의 흐름특성을 검토하는 것으로 침수 수위조건은 유량공급조건의 변화를 통해 재현하였다. 본 연구에 사용된 LSPIV 기법은 하도내 입자투여를 통해 영상을 이용하여 입자간 이동속도를 분석하여 흐름장을 분석하는 것으로 본 연구에서 사용된 입자는 강랭이를 사용하였으며, 영상촬영은 크레인을 이용하여 캠코더를 이용하여 수로 상부에서 흐름영상을 취득하였다. 실험수로는 저면폭 3m, 만제폭 11m 사면경사 1:2의 구조로 이루어진 사다리꼴 형태의 직선수로로 이루어져 있다. 식생패치는 동일수로내 상부기준 32m 간격으로 설치되어 있으며 패치의 크기는 $4{\times}1.5m$의 크기로 이루어져 있다. 그림 1은 상공에서 촬영된 영상을 나타낸 그림으로 실험수로 및 LSPIV 분석을 위해 투하된 입자를 나타내는 그림이다. PIV 분석프로그램을 이용하여 분석된 식생대 및 하도영역에서의 유속장은 그림 2와 같다. 영상분석은 30초영상(900 frame)을 이용하여 분석하였다. 실험결과 식생패치 설치지점에서는 주흐름이 발생하는 우안측에서 높은 유속이 발생하고 약간의 편향된 흐름이 나타나는 것으로 확인되었다. 식생후면에서는 식생으로 인한 흐름의 차단 및 스크린효과로 인해 유속의 저감되는 것을 확인할 수 있다. 밀도에 따른 패치 후면부에서는 높은 밀도를 갖는 첫 번째 패치에서 유속의 저감효과가 높게 나타났으나 유량조건에 차이가 발생하는 것으로 나타났는데 이는 수위상승으로 인한 식생의 침수면적과 관계가 있는 것으로 판단된다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.8-16
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• 2011

Image inpainting is a technique for removing damaged regions and reconstructing them with visually plausible backgrounds. However, if size of the damaged regions for reconstructing is large, the unexpected results can be obtained due to disconnected structures within reconstructed regions. In this paper, by considering spatial distance information between candidate patches and a damaged patch as well as pixel value difference, an exemplar-based image inpainting using multiple patches is proposed. In conventional exemplar-based image inpainting method, implausible results such as blocking effects or repetition of reconstructed patch may occur by using inappropriately selected single patch. To improve the exemplar-based method, the weighted sum of multiple patches considering both the spatial distance and the pixel value difference between the target patch and the candidate patches is utilized. Experimental results have shown that the proposed method produces better performance of image inpainting than the existing method.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.295-302
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• 2010

Example-based super resolution(EBSR) is a method to reconstruct high-resolution images by learning patch-wise correspondence between high-resolution and low-resolution images. It can reconstruct a high-resolution from just a single low-resolution image. However, when it is applied to a text image whose font type and size are different from those of training images, it often produces lots of noise. The primary reason is that, in the patch matching step of the reconstruction process, input patches can be inappropriately matched to the high-resolution patches in the patch dictionary. In this paper, we propose a new patch matching method to overcome this problem. Using an image observation model, it preserves the correlation between the input and the output images. Therefore, it effectively suppresses spurious noise caused by inappropriately matched patches. This does not only improve the quality of the output image but also allows the system to use a huge dictionary containing a variety of font types and sizes, which significantly improves the adaptability to variation in font type and size. In experiments, the proposed method outperformed conventional methods in reconstruction of multi-font and multi-size images. Moreover, it improved recognition performance from 88.58% to 93.54%, which confirms the practical effect of the proposed method on recognition performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.177-185
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• 2022

A method of restoring the occluded area was proposed by referring to images taken with the same types of sensors on high-resolution optical satellite images through deep learning. For the natural continuity of the simulated image with the occlusion region and the surrounding image while maintaining the pixel distribution of the original image as much as possible in the patch segmentation image, CycleGAN (Cycle Generative Adversarial Network) method with ConvNeXt block applied was used to analyze three experimental regions. In addition, We compared the experimental results of a training patch size of 512*512 pixels and a 1024*1024 pixel size that was doubled. As a result of experimenting with three regions with different characteristics,the ConvNeXt CycleGAN methodology showed an improved R² value compared to the existing CycleGAN-applied image and histogram matching image. For the experiment by patch size used for training, an R² value of about 0.98 was generated for a patch of 1024*1024 pixels. Furthermore, As a result of comparing the pixel distribution for each image band, the simulation result trained with a large patch size showed a more similar histogram distribution to the original image. Therefore, by using ConvNeXt CycleGAN, which is more advanced than the image applied with the existing CycleGAN method and the histogram-matching image, it is possible to derive simulation results similar to the original image and perform a successful simulation.