Eliminating structure and intensity misalignment in image stitching
Eliminating structure and intensity misalignment in image stitching
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DOI：
10.1109/iccv.2005.87
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发表时间：
2005-10
期刊：
影响因子：
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通讯作者：
Jiaya Jia;Chi-Keung Tang
中科院分区：
文献类型：
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作者：
Jiaya Jia;Chi-Keung Tang
本文的目标是在全局配准的前提下，实现图像的无缝拼接，以消除由于严重的亮度差异、图像失真和结构错位等因素造成的明显的视觉伪影。我们的方法是基于结构变形和传播，同时保持整体外观的亲和力的结果输入图像。这种新的方法被证明是有效的，在解决上述问题，并已发现应用在马赛克去重影，图像融合和强度校正。我们的新方法包括以下主要过程。首先，鲁棒地检测显著特征或结构，并沿输入图像之间的最佳分割边界沿着对齐。从这些特征中，我们导出稀疏变形向量，以均匀地编码底层结构和强度未对准。然后，通过在图像梯度域中求解相关联的拉普拉斯方程，这些稀疏变形线索将被鲁棒且平滑地传播到目标图像的内部。我们提出了令人信服的结果表明，我们的方法可以处理显着的结构和强度错位的图像拼接
The aim of this paper is to achieve seamless image stitching for eliminating obvious visual artifact caused by severe intensity discrepancy, image distortion and structure misalignment, given that the input images are globally registered. Our approach is based on structure deformation and propagation while maintaining the overall appearance affinity of the result to the input images. This new approach is proven to be effective in solving the above problems, and has found applications in mosaic deghosting, image blending and intensity correction. Our new method consists of the following main processes. First, salient features or structures are robustly detected and aligned along the optimal partitioning boundary between the input images. From these features, we derive sparse deformation vectors to to uniformly encode the underlying structure and intensity misalignment. These sparse deformation cues will then be propagated robustly and smoothly into the interior of the target image by solving the associated Laplace equations in the image gradient domain. We present convincing results to show that our method can handle significant structure and intensity misalignment in image stitching