Defocus map estimation from a single image
In this paper, we address the challenging problem of recovering the defocus map from a single image. We present a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations. The input defocused image is re-blurred using a Gaussian kernel and the defocus...
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Published in | Pattern recognition Vol. 44; no. 9; pp. 1852 - 1858 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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Elsevier Ltd
01.09.2011
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Abstract | In this paper, we address the challenging problem of recovering the defocus map from a single image. We present a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations. The input defocused image is re-blurred using a Gaussian kernel and the defocus blur amount can be obtained from the ratio between the gradients of input and re-blurred images. By propagating the blur amount at edge locations to the entire image, a full defocus map can be obtained. Experimental results on synthetic and real images demonstrate the effectiveness of our method in providing a reliable estimation of the defocus map.
► We address the challenging problem of defocus estimation from a single image. ► The defocus blur is estimated at edge locations from the gradient ratio between tlie original and re-blurred input image. ► A hill defocus map is obtained by propagating the blur amount at edges to the entire image using soft matting. ► We also discuss the ambiguities in defocus estimation and the relationship between defocus map and depth. |
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AbstractList | In this paper, we address the challenging problem of recovering the defocus map from a single image. We present a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations. The input defocused image is re-blurred using a Gaussian kernel and the defocus blur amount can be obtained from the ratio between the gradients of input and re-blurred images. By propagating the blur amount at edge locations to the entire image, a full defocus map can be obtained. Experimental results on synthetic and real images demonstrate the effectiveness of our method in providing a reliable estimation of the defocus map.
► We address the challenging problem of defocus estimation from a single image. ► The defocus blur is estimated at edge locations from the gradient ratio between tlie original and re-blurred input image. ► A hill defocus map is obtained by propagating the blur amount at edges to the entire image using soft matting. ► We also discuss the ambiguities in defocus estimation and the relationship between defocus map and depth. In this paper, we address the challenging problem of recovering the defocus map from a single image. We present a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations. The input defocused image is re-blurred using a Gaussian kernel and the defocus blur amount can be obtained from the ratio between the gradients of input and re-blurred images. By propagating the blur amount at edge locations to the entire image, a full defocus map can be obtained. Experimental results on synthetic and real images demonstrate the effectiveness of our method in providing a reliable estimation of the defocus map. We address the challenging problem of defocus estimation from a single image. The defocus blur is estimated at edge locations from the gradient ratio between tlie original and re-blurred input image. A hill defocus map is obtained by propagating the blur amount at edges to the entire image using soft matting. We also discuss the ambiguities in defocus estimation and the relationship between defocus map and depth. |
Author | Zhuo, Shaojie Sim, Terence |
Author_xml | – sequence: 1 givenname: Shaojie surname: Zhuo fullname: Zhuo, Shaojie email: zhuoshao@comp.nus.edu.sg – sequence: 2 givenname: Terence surname: Sim fullname: Sim, Terence |
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Keywords | Gaussian gradient Image processing Defocus blur Defocus magnification Defocus map |
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Snippet | In this paper, we address the challenging problem of recovering the defocus map from a single image. We present a simple yet effective approach to estimate the... |
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SubjectTerms | Ambiguity Defocus blur Defocus magnification Defocus map Estimates Gaussian Gaussian gradient Image processing Kernels Pattern recognition Propagation Recovering |
Title | Defocus map estimation from a single image |
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