• Title/Summary/Keyword: 마르코프 랜덤 장

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MRF-based Adaptive Noise Detection Algorithm for Image Restoration (영상 복원을 위한 MRF 기반 적응적 노이즈 탐지 알고리즘)

  • Nguyen, Tuan-Anh;Hong, Min-Cheol
    • Journal of Korea Multimedia Society
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    • v.16 no.12
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    • pp.1368-1375
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    • 2013
  • In this paper, we presents a spatially adaptive noise detection and removal algorithm. Under the assumption that an observed image and the additive noise have Gaussian distribution, the noise parameters are estimated with local statistics, and the parameters are used to define the constraints on the noise detection process, where the first order Markov Random Field (MRF) is used. In addition, an adaptive low-pass filter having a variable window sizes defined by the constraints on noise detection is used to control the degree of smoothness of the reconstructed image. Experimental results demonstrate the capability of the proposed algorithm.

Fast Blind Image Denoising Algorithm Based on Estimating Noise Parameters (노이즈 매개변수 예측 기반 고속 노이즈 제거 방식)

  • Nguyen, Tuan-Anh;Kim, Beomsu;Hong, Min-Cheol
    • Journal of IKEEE
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    • v.18 no.4
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    • pp.523-531
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    • 2014
  • In this paper, a fast single image blind denoising algorithm is presented, where noise parameters are estimated by local statistics of an observed degraded image without a prior information about the additive noise. The estimated noise parameters are used to define the constraints on the noise detection which is coupled with the 1st-order Markov Random Field. In addition, an adaptive modified weighted Gaussian filter is introduced, where variable window sizes and weighting coefficients defined by the constraints are used to control the degree of the smoothness of the reconstructed image. The experimental results demonstrate the capability of the proposed algorithm. Please put the abstract of paper here.

Bilayer Segmentation of Consistent Scene Images by Propagation of Multi-level Cues with Adaptive Confidence (다중 단계 신호의 적응적 전파를 통한 동일 장면 영상의 이원 영역화)

  • Lee, Soo-Chahn;Yun, Il-Dong;Lee, Sang-Uk
    • Journal of Broadcast Engineering
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    • v.14 no.4
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    • pp.450-462
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    • 2009
  • So far, many methods for segmenting single images or video have been proposed, but few methods have dealt with multiple images with analogous content. These images, which we term consistent scene images, include concurrent images of a scene and gathered images of a similar foreground, and may be collectively utilized to describe a scene or as input images for multi-view stereo. In this paper, we present a method to segment these images with minimum user input, specifically, manual segmentation of one image, by iteratively propagating information via multi-level cues with adaptive confidence depending on the nature of the images. Propagated cues are used as the bases to compute multi-level potentials in an MRF framework, and segmentation is done by energy minimization. Both cues and potentials are classified as low-, mid-, and high- levels based on whether they pertain to pixels, patches, and shapes. A major aspect of our approach is utilizing mid-level cues to compute low- and mid- level potentials, and high-level cues to compute low-, mid-, and high- level potentials, thereby making use of inherent information. Through this process, the proposed method attempts to maximize the amount of both extracted and utilized information in order to maximize the consistency of the segmentation. We demonstrate the effectiveness of the proposed method on several sets of consistent scene images and provide a comparison with results based only on mid-level cues [1].

Joint Segmentation of Multi-View Images by Region Correspondence (영역 대응을 이용한 다시점 영상 집합의 통합 영역화)

  • Lee, Soo-Chahn;Kwon, Dong-Jin;Yun, Il-Dong;Lee, Sang-Uk
    • Journal of Broadcast Engineering
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    • v.13 no.5
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    • pp.685-695
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    • 2008
  • This paper presents a method to segment the object of interest from a set of multi-view images with minimal user interaction. Specifically, after the user segments an initial image, we first estimate the transformations between foreground and background of the segmented image and the neighboring image, respectively. From these transformations, we obtain regions in the neighboring image that respectively correspond to the foreground and the background of the segmented image. We are then able to segment the neighboring image based on these regions, and iterate this process to segment the whole image set. Transformation of foregrounds are estimated by feature-based registration with free-form deformation, while transformation of backgrounds are estimated by homography constrained to affine transformation. Here, both are based on correspondence point pairs. Segmentation is done by estimating pixel color distributions and defining a shape prior based on the obtained foreground and background regions and applying them to a Markov random field (MRF) energy minimization framework for image segmentation. Experimental results demonstrate the effectiveness of the proposed method.