• Title/Summary/Keyword: 디블러링

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Video Motion Deblurring Using Adjacent Unblurred Frame (블러가 발생하지 않은 인접한 프레임을 이용한 동영상 디블러링 기법)

  • Lee, Dong-Bok;Jeong, Shin-Cheol;Choi, Ik-Hyun;Song, Byung-Cheol
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.53-54
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    • 2011
  • 본 논문에서 우리는 동영상에서 인접한 블러되지 않은 참조 프레임을 이용하여 모션 블러를 제거하는 기법을 제안한다. 기존의 디블러링 방법들은 주로 단일 영상을 이용한 방법들로 정확한 커널을 예측하는 것과 원본 영상에 준하는 영상을 복원하는 것에 한계가 존재한다. 하지만 동영상에서 부분적인 프레임에만 블러가 발생한 특수한 경우에는 인접한 위치에 존재하는 블러되지 않은 프레임을 활용하는 것이 가능하다. 제안하는 방법은 블러된 프레임과 인접한 위치에 존재하는 블러되지 않은 프레임 사이에 움직임을 추정하고, 움직임 보상된 영상을 이용하여 커널을 추정한다. 또한 움직임 오차에 따른 잔여 오차 성분에 대해서만 디컨벌루션을 적용하여 물결현상이 억제된 최종적인 결과 영상을 생성한다. 실험 결과는 제안한 방법이 기존의 디블러링 기법에 비해 에지 부분을 잘 복원시키면서 물결현상은 감소된 보다 우수한 디블러링 결과를 가져오는 것을 보여준다.

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A Deblurring Algorithm Combined with Edge Directional Color Demosaicing for Reducing Interpolation Artifacts (컬러 보간 에러 감소를 위한 에지 방향성 컬러 보간 방법과 결합된 디블러링 알고리즘)

  • Yoo, Du Sic;Song, Ki Sun;Kang, Moon Gi
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.7
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    • pp.205-215
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    • 2013
  • In digital imaging system, Bayer pattern is widely used and the observed image is degraded by optical blur during image acquisition process. Generally, demosaicing and deblurring process are separately performed in order to convert a blurred Bayer image to a high resolution color image. However, the demosaicing process often generates visible artifacts such as zipper effect and Moire artifacts when performing interpolation across edge direction in Bayer pattern image. These artifacts are emphasized by the deblurring process. In order to solve this problem, this paper proposes a deblurring algorithm combined with edge directional color demosaicing method. The proposed method is consisted of interpolation step and region classification step. Interpolation and deblurring are simultaneously performed according to horizontal and vertical directions, respectively during the interpolation step. In the region classification step, characteristics of local regions are determined at each pixel position and the directionally obtained values are region adaptively fused. Also, the proposed method uses blur model based on wave optics and deblurring filter is calculated by using estimated characteristics of local regions. The simulation results show that the proposed deblurring algorithm prevents the boosting of artifacts and outperforms conventional approaches in both objective and subjective terms.

Fast Multiple-Image-Based Deblurring Method (다중 영상 기반의 고속 처리용 디블러링 기법)

  • Son, Chang-Hwan;Park, Hyung-Min
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.49 no.4
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    • pp.49-57
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    • 2012
  • This paper presents a fast multiple-image-based deblurring method that decreases the computation loads in the image deblurring, enhancing the sharpness of the textures or edges of the restored images. First, two blurred images with some blurring artifacts and one noisy image including severe noises are consecutively captured under a relatively long and short exposures, respectively. To improve the processing speeds, the captured multiple images are downsampled at the ratio of two, and then a way of estimating the point spread function(PSF) based on the image or edge patches extracted from the whole images, is introduced. The method enables to effectively reduce the computation time taken in the PSF prediction. Next, the texture-enhanced image deblurring method of supplementing the ability of the texture representation degraded by the downsampling of the input images, is developed and then applied. Finally, to get the same image size as the original input images, an upsampling method of utilizing the sharp edges of the captured noisy image is applied. By using the proposed method, the processing times taken in the image deblurring, which is the main obstacle of its application to the digital cameras, can be shortened, while recovering the fine details of the textures or edge components.

Image Deblurring Based on ADMM and Deep CNN Denoiser Image Prior (ADMM과 깊은 합성곱 신경망 잡음 제거기 이미지 Prior에 기반한 이미지 디블러링)

  • Kwon, Junhyeong;Soh, Jae Woong;Cho, Nam Ik
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.680-683
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    • 2020
  • 오래 전부터 모델 기반 최적화 방법이 이미지 디블러링을 위해 널리 사용되어 왔고, 최근에는 학습 기반 기술이 영상 디블러링에서 좋은 성과를 보이고 있다. 본 논문은 ADMM과 깊은 합성곱 신경망 잡음 제거기 이미지 prior를 이용하여 모델 기반 최적화 방법의 장점과 학습 기반 방법의 장점을 모두 활용할 수 있는 방법을 제안한다. 본 방법을 이용하여 기존 방법보다 더 좋은 디블러링 성능을 얻을 수 있었다.

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A Deblurring Algorithm using Diffusion Equation (확산방정식 이용한 디블러링 알고리즘)

  • Lee, In-Jung;Chang, Hee-Dong
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.177-180
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    • 2009
  • 볼록거울을 사용하여 CCTV시스템을 만들면 카메라 수를 줄이는 효과가 있다. 이 경우 볼록거울 영상은 휜 영상이므로 평면영상처럼 변환해야 한다. 이 경우에, 중앙에 비추인 영상은 평면 영상으로 변환 후에도 왜곡이 거의 없지만 거울의 테두리 부근에서 얻은 영상을 변환하면 왜곡이 심하게 나타나서 영상 내의 물체를 식별하기가 어려워진다. 렌즈의 초점이 거울의 중심부에 맞춰져 있기 때문에 변에 있는 영상은 픽셀들 사이에 겹침이 일어나기 때문이다. 본 논문에서는 픽셀들 사이의 광학적 겹침을 극복하기 위해 확산 방정식의 후진대입 해를 사용하였다. 결과를 분석하기 위해 PSNR값을 조사하였더니 제안된 방법은 N. Moayeri의 방법보다 4 dB 정도 개선되었다.

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Multi-View Image Deblurring for 3D Shape Reconstruction (3차원 형상 복원을 위한 다중시점 영상 디블러링)

  • Choi, Ho Yeol;Park, In Kyu
    • Journal of the Institute of Electronics and Information Engineers
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    • v.49 no.11
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    • pp.47-55
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    • 2012
  • In this paper, we propose a method to reconstruct accurate 3D shape object by using multi-view images which are disturbed by motion blur. In multi-view deblurring, more precise PSF estimation can be done by using the geometric relationship between multi-view images. The proposed method first estimates initial 2D PSFs from individual input images. Then 3D PSF candidates are projected on the input images one by one to find the best one which are mostly consistent with the initial 2D PSFs. 3D PSF consists with direction and density and it represents the 3D trajectory of object motion. 야to restore 3D shape by using multi-view images computes the similarity map and estimates the position of 3D point. The estimated 3D PSF is again projected to input images and they replaces the intial 2D PSFs which are finally used in image deblurring. Experimental result shows that the quality of image deblurring and 3D reconstruction improves significantly compared with the result when the input images are independently deblurred.

De-blurring Algorithm for Performance Improvement of Searching a Moving Vehicle on Fisheye CCTV Image (어안렌즈사용 CCTV이미지에서 차량 정보 수집의 성능개선을 위한 디블러링 알고리즘)

  • Lee, In-Jung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.4C
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    • pp.408-414
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    • 2010
  • When we are collecting traffic information on CCTV images, we have to install the detect zone in the image area during pan-tilt system is on duty. An automation of detect zone with pan-tilt system is not easy because of machine error. So the fisheye lens attached camera or convex mirror camera is needed for getting wide area images. In this situation some troubles are happened, that is a decreased system speed or image distortion. This distortion is caused by occlusion of angled ray as like trembled snapshot in digital camera. In this paper, we propose two methods of de-blurring to overcome distortion, the one is image segmentation by nonlinear diffusion equation and the other is deformation for some segmented area. As the results of doing de-blurring methods, the de-blurring image has 15 decibel increased PSNR and the detection rate of collecting traffic information is more than 5% increasing than in distorted images.

A Video Deblurring Algorithm based on Sharpness Metric for Uniform Sharpness between Frames (프레임 간 선명도 균일화를 위한 선명도 메트릭 기반의 동영상 디블러링 알고리즘)

  • Lee, Byung-Ju;Lee, Dong-Bok;Song, Byung Cheol
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.4
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    • pp.127-136
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    • 2013
  • This paper proposes a video deblurring algorithm which maintains uniform sharpness between frames. Unlike the previous algorithms using fixed parameters, the proposed algorithm keeps uniform sharpness by adjusting parameters for each frame. First, we estimate the initial blur kernel and perform deconvolution, then measure the sharpness of the deblurred image. In order to maintain uniform sharpness, we adjust the regularization parameter and kernel according to the examined sharpness, and perform deconvolution again. The experimental results show that the proposed algorithm achieves outstanding deblurring results while providing consistent sharpness.