In neurosurgery, the patient's pulse and breathing motion, as well as technical and physiological artifacts during Infrared Thermography (IRT) acquisition cause difficulties; accordingly, a robust and adjusted motion correction method is required. In this paper, a comparison of frequency-/filter- and intensity-based approaches for the Horn-Schunck method, the Lucas-Kanade method, the OA-CLG method, the phase-based method, and FIR bandstop filter is provided. Firstly, images are registered with respect to a reference image, and image enhancement as a preprocessing step is carried out for those methods that rely on brightness constancy assumption (BCA) only. In the second step, motion estimation and compensation for local motion are applied to suppress small motion, i.e., pulse and breathing motion correction. The processing step can be done either with the frequency-/filter- or intensity-based approaches. Comparisons are performed using three types of datasets namely, the human IRT brain data (clinical cases), semi-synthetic IRT brain data, and phantom IRT data. Results from semi-synthetic and phantom IRT data indicate that the intensity-based methods are able to estimate and compensate pulse and breathing motion artifacts while preserving all the image details, structures, and spatial resolution after motion correction. The human brain datasets demonstrate that motion correction can be a beneficial step in IRT imaging during neurosurgery, obtaining better correction metric-wise in each four performance measurements.