手持式全光学相机的视差容限光场拼接。

IF 10.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xin Jin, Pei Wang, Qionghai Dai
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引用次数: 0

摘要

光场(LF)拼接是改善手持式全光学摄像机视场(FOV)的一种潜在解决方案。现有的光场拼接方法无法为深度变化较大的场景提供精确的配准。本文提出了一种新颖的 LF 拼接方法,可以更灵活、更准确地处理 LF 中的视差。首先,本文提出了一个深度图层图(DLM),以保证每个深度图层上都有足够的特征点。对于深度不确定的区域,提出了基于 LF 空间相关性分析的超像素层图(SLM),以细化深度层的分配。然后,提出了基于 DLM-SLM 的 LF 配准方法,以准确推导出与位置相关的同构变换,并在没有视差干扰的情况下将 LF warp 到相应的位置。进一步应用 4D 图形切割来融合配准结果,以获得更高的 LF 空间连续性和角度连续性。对不同场景进行了水平、垂直和多 LF 拼接测试,结果表明,在拼接 LF 的主观质量、拼接 LF 的外极面图像一致性以及拼接 LF 与输入 LF 之间的透视平均相关性等方面,所提出的方法都具有卓越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallax Tolerant Light Field Stitching for Hand-held Plenoptic Cameras.

Light field (LF) stitching is a potential solution to improve the field of view (FOV) for hand-held plenoptic cameras. Existing LF stitching methods cannot provide accurate registration for scenes with large depth variation. In this paper, a novel LF stitching method is proposed to handle parallax in the LFs more flexibly and accurately. First, a depth layer map (DLM) is proposed to guarantee adequate feature points on each depth layer. For the regions of nondeterministic depth, superpixel layer map (SLM) is proposed based on LF spatial correlation analysis to refine the depth layer assignments. Then, DLM-SLM-based LF registration is proposed to derive the location dependent homography transforms accurately and to warp LFs to its corresponding position without parallax interference. 4D graph-cut is further applied to fuse the registration results for higher LF spatial continuity and angular continuity. Horizontal, vertical and multi-LF stitching are tested for different scenes, which demonstrates the superior performance provided by the proposed method in terms of subjective quality of the stitched LFs, epipolar plane image consistency in the stitched LF, and perspective-averaged correlation between the stitched LF and the input LFs.

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来源期刊
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing 工程技术-工程:电子与电气
CiteScore
20.90
自引率
6.60%
发文量
774
审稿时长
7.6 months
期刊介绍: The IEEE Transactions on Image Processing delves into groundbreaking theories, algorithms, and structures concerning the generation, acquisition, manipulation, transmission, scrutiny, and presentation of images, video, and multidimensional signals across diverse applications. Topics span mathematical, statistical, and perceptual aspects, encompassing modeling, representation, formation, coding, filtering, enhancement, restoration, rendering, halftoning, search, and analysis of images, video, and multidimensional signals. Pertinent applications range from image and video communications to electronic imaging, biomedical imaging, image and video systems, and remote sensing.
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