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Defocus map estimation from a single image using improved likelihood feature and edge-based basis
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DOI：10.1016/j.patcog.2020.107485.png)
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摘要
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Defocus map estimation (DME) is very useful in many computer vision applications and has drawn much attention in recent years. Edge-based DME methods can generate sharp defocus discontinuities but usually suffer from textures of the input image. Region-based methods are free of textures but cannot catch the defocus discontinuities very well. In this paper, we propose a DME method combining edge-based and region-based methods together to keep their respective advantages while eliminating the shortcomings. The combination is achieved via regression tree fields (RTF). In an RTF, the input feature and the linear basis are of vital importance. For our RTF, they are obtained as follows. (i) Two orthogonal gradient operators with the corresponding subsets of Gabor filters are employed in localized 2D frequency analysis to generate accurate likelihood, and the first K highest local maximums of likelihood are sent to an RTF as input feature. (ii) At the same time, the input image is processed by three edge-based methods and the results serve as the linear basis of RTF. The experiments demonstrate that the proposed method outperforms state-of-the-art DME methods. Moreover, the proposed method can be readily applied to defocused image deblurring and defocus blur detection. (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Defocus map estimation
Regression tree fields
Localized 2D frequency analysis
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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