Department of Company Engineering, Military Technical College, Cairo, Egypt.
IEEE Trans Image Process. 2012 Mar;21(3):1046-60. doi: 10.1109/TIP.2011.2167344. Epub 2011 Sep 8.
We present an image registration model for image sets with arbitrarily shaped local illumination variations between images. Any nongeometric variations tend to degrade the geometric registration precision and impact subsequent processing. Traditional image registration approaches do not typically account for changes and movement of light sources, which result in interimage illumination differences with arbitrary shape. In addition, these approaches typically use a least-square estimator that is sensitive to outliers, where interimage illumination variations are often large enough to act as outliers. In this paper, we propose an image registration approach that compensates for arbitrarily shaped interimage illumination variations, which are processed using robust M -estimators tuned to that region. Each M-estimator for each illumination region has a distinct cost function by which small and large interimage residuals are unevenly penalized. Since the segmentation of the interimage illumination variations may not be perfect, a segmentation confidence weighting is also imposed to reduce the negative effect of mis-segmentation around illumination region boundaries. The proposed approach is cast in an iterative coarse-to-fine framework, which allows a convergence rate similar to competing intensity-based image registration approaches. The overall proposed approach is presented in a general framework, but experimental results use the bisquare M-estimator with region segmentation confidence weighting. A nearly tenfold improvement in subpixel registration precision is seen with the proposed technique when convergence is attained, as compared with competing techniques using both simulated and real data sets with interimage illumination variations.
我们提出了一种用于图像集的图像配准模型，这些图像集具有图像之间任意形状的局部光照变化。任何非几何变化都会降低几何配准精度，并影响后续处理。传统的图像配准方法通常不考虑光源的变化和运动，这会导致图像之间具有任意形状的光照差异。此外，这些方法通常使用最小二乘估计器，该估计器对异常值很敏感，而图像之间的光照变化通常足够大，足以作为异常值。在本文中，我们提出了一种图像配准方法，该方法可以补偿任意形状的图像间光照变化，这些变化使用针对该区域进行调整的稳健 M 估计器进行处理。每个光照区域的每个 M 估计器都有一个独特的代价函数，通过该代价函数，可以不均匀地惩罚小和大的图像间残差。由于图像间光照变化的分割可能不完美，因此还施加了分割置信度加权，以减少光照区域边界周围误分割的负面影响。所提出的方法采用粗到精的迭代框架，其收敛速度类似于竞争的基于强度的图像配准方法。所提出的方法以一般框架呈现，但实验结果使用带区域分割置信度加权的双平方 M 估计器。与使用竞争技术的方法相比，当达到收敛时，所提出的技术在模拟和具有图像间光照变化的真实数据集上可将亚像素配准精度提高近十倍。
