Segmentation-based spatially adaptive motion blur removal and its application to surveillance systems

Various image restoration methods have been studied for removing space-variant motion blur such as iterative and POCS method. However, their computational complexity of the methods, such as regularized iteration and POCS method, is so high that they can hardly be implemented in real-time. In this paper, we address the method to reduce the computational complexity by selecting the region to be restored. The primary application area of the proposed method is a surveillance system which requires accurate object extraction, identification, and tracking functions. To remove motion blur, we propose a new spatially adaptive regularized iterative image restoration algorithm. Experimental results show the the proposed algorithm can efficiently remove space-variant motion blur with significantly reduced computational overhead.