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Journal ArticleDOI

Defocus map estimation from a single image via spectrum contrast.

Chang Tang, +2 more
- 15 May 2013 - 
- Vol. 38, Iss: 10, pp 1706-1708
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TLDR
The proposed method takes into consideration not only the affect of light refraction but also the blur texture of an image, and is more reliable in defocus map estimation compared to various state-of-the-art methods.
Abstract
We present an effective method for defocus map estimation from a single natural image. It is inspired by the observation that defocusing can significantly affect the spectrum amplitude at the object edge locations in an image. By establishing the relationship between the amount of spatially varying defocus blur and spectrum contrast at edge locations, we first estimate the blur amount at these edge locations, then a full defocus map can be obtained by propagating the blur amount at edge locations over the entire image with a nonhomogeneous optimization procedure. The proposed method takes into consideration not only the affect of light refraction but also the blur texture of an image. Experimental results demonstrate that our proposed method is more reliable in defocus map estimation compared to various state-of-the-art methods.

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Citations
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Journal ArticleDOI

Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure

TL;DR: This paper proposes a novel boundary finding based multi-focus image fusion algorithm, in which the task of detecting the focused regions is treated as finding the boundaries between the focused and defocused regions from the source images.
Proceedings ArticleDOI

Deep Defocus Map Estimation Using Domain Adaptation

TL;DR: The first end-to-end convolutional neural network (CNN) architecture, Defocus Map Estimation Network (DMENet), for spatially varying defocus map estimation is proposed, which uses domain adaptation that transfers the features of real defocused photos into those of synthetically blurred ones.
Proceedings ArticleDOI

Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes

TL;DR: In this paper, a high-frequency multiscale fusion and sort transform (HiFST) of gradient magnitudes was proposed to detect spatially-varying blur.
Journal ArticleDOI

Edge-Based Defocus Blur Estimation With Adaptive Scale Selection

TL;DR: A new edge-based method for spatially varying defocus blur estimation using a single image based on reblurred gradient magnitudes is presented and a fast guided filter is used to propagate the sparse blur map through the whole image.
Journal ArticleDOI

A Spectral and Spatial Approach of Coarse-to-Fine Blurred Image Region Detection

TL;DR: A novel iterative updating mechanism is proposed to refine the blur map from coarse to fine by exploiting the intrinsic relevance of similar neighbor image regions and can partially resolve the problem of differentiating an in-focus smooth region and a blurred smooth region.
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