Adaptive weighted guided image filtering for depth enhancement in shape-from-focus. (November 2022)

• Record Type: Journal Article
• Authors: Li, Yuwen; Li, Zhengguo; Zheng, Chaobing; Wu, Shiqian
• Is Part Of: Pattern recognition. Volume 131(2022)
• Publication Date: 2022-11
• Subjects: Shape from focus -- Depth enhancement -- Adaptive weighted guided image filtering -- Edge-preserving -- Robustness; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108900
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We proposed a novel AdaptiveWeighted Guided Image Filtering algorithm which outperforms the existing guided filters to preserve accurate edges. We designed four special cases of the adaptive adjustment algorithm to remove the noise while enhancing depth details. We proposed a new strategy based on AWGIF algorithm for depth enhancement in Shape-From-Focus. Our proposed method are applied to the emerging AR/VR on hand-held devices in real-time. Abstract: Existing shape from focus (SFF) techniques cannot preserve depth edges and fine structural details from a sequence of multi-focus images. Moreover, noise in the sequence affects the accuracy of the depth map. In this paper, a novel depth enhancement algorithm for the SFF based on an adaptive weighted guided image filtering (AWGIF) is proposed to address the above issues. The AWGIF is applied to decompose an initial depth map estimated by the traditional SFF into base and detail layers. In order to preserve the edges accurately in the refined depth map, the guidance image is constructed from the sequence, and the coefficient of the AWGIF is utilized to suppress the noise while enhancing the fine depth details. Experiments on real and synthetic objects demonstrate the superiority of our algorithm in terms of anti-noise, and the ability to preserve depth edges and fine structural details w.r.t. existing methods.