Geometric Image Registration under Locally Variant Illuminations Using Huber M-estimator

In this paper, we extend our previous work on presenting a registration model for images having arbitrarily-shaped locally variant illuminations from shadows to multiple shading levels. These variations tend to degrade the performance of geometric registration and impact subsequent processing. Often, traditional registration models use a leastsquares estimator that is sensitive to outliers. Instead, we propose using a robust Huber M -estimator to increase the geometric registration accuracy (GRA). We demonstrate the proposed model and compare it to other models on simulated and real data. This modification shows clear improvements in terms of GRA and illumination correction.