In brain surgery, respiration motion, outliers, and occlusions create artefacts in Infrared Thermography (IRT) imaging. In this paper, we propose a robust method to handle multiple motion, outliers, and occlusions in IRT images which consists of two phases: preprocessing and image motion estimation. In the preprocessing phase, the Region of Interest (RoI) segmentation is employed to extract the brain cortex only. Thereafter, the Phase Correlation method is employed to compensate for large motion followed by occlusion masking based on an approach applying Cellular Nonlinear Networks (CNN). Next, intensity adjustment is made with respect to the reference image. Then, a Gaussian filter is applied. In the following phase, the image motion is estimated by employing Combined Local-Global (CLG) optical flow method. In order to find the best regularization coefficient for the spatial coherency term and the number of iterations for recursive optical flow estimation, we generated ground truth and evaluated the accuracy of the estimated motion vectors based on Average Angular Error (AAE) and Average Magnitude Error (AME). The efficiency improvement of the proposed method was tested on 1024 IRT images based on different comparisons. Thereby, the proposed method shows promising results for motion estimation and correction application in brain surgery.