In this paper, a new respiration and heartbeat motion artifacts correction method for thermographic images in neurosurgery is presented. Aims to solve the ambiguity of the respiration and heartbeat motion artifacts correction of thermographic images in neurosurgery. The proposed method consists of two main steps; firstly, a complex steerable pyramid is employed to separate the local wavelets of the phase for each thermographic image. Thereafter, temporal filtering is carried out to pass respiration and heartbeat frequencies and reject other frequencies outside that range followed by image reconstruction. Secondly, the optimized and adjusted-combined local and global (OA-CLG) method is applied to detect respiration and heartbeat motion artifacts only. Afterwards, a bicubic interpolation is carried out to compensate the estimated motion artifacts. To evaluate the proposed method's performance and accuracy, 10 clinical thermographic datasets were examined. Results show that with the proposed method respiration and heartbeat motion artifacts can be corrected while preserving the image structures, properties, spatial resolution, and maintaining temperature values.