Defocus map estimation from a single image using improved likelihood feature and edge-based basis. (November 2020)

• Record Type: Journal Article
• Authors: Liu, Shaojun; Liao, Qingmin; Xue, Jing-Hao; Zhou, Fei
• Is Part Of: Pattern recognition. Volume 107(2020:Nov.)
• Publication Date: 2020-11
• Subjects: Defocus map estimation -- Regression tree fields -- Localized 2D frequency analysis; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107485
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Edge-based and region-based methods are combined via RTF with linear basis. Orthogonal gradients with Gabor filter subsets can obtain more accurate likelihood. First K highest local maximums are effective to serve as the input feature of RTF. Proposed method outperforms state-of-the-art DME methods. Proposed method is readily applied to image deblurring and defocus blur detection. Abstract: 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.