This paper presents a new method for image interpolation based on truncated projections onto convex sets (POCS). By using the convergence property to properly defined convex sets, the proposed algorithm can restore high frequency details in the original high resolution image. In order to apply the POCS method to the interpolation procedure, we first present a two-dimensional separable image degradation model for a low resolution imaging system. According to the model, we propose a truncated POCS-based spatial interpolation algorithm for image sequences. Experimental results with synthetic and real image sequence show that the proposed algorithm gives indiscernible interpolation performance compared with the conventional POCS-base algorithm, while it significantly reduces computational complexity and is suitable for processing image sequences.

A new motion analysis method and an image restoration process for removing motion blur are proposed. Motion analysis includes the motion estimation and motion-based segmentation. Based on the analysis, we can obtain an image divided into multiple segments with different point spread functions. For removing motion blur, we propose an image degradation model for the motion with an arbitrary direction and a regularized iterative restoration method. By using the proposed degradation model and the restoration method, we can efficiently remove the space-variant motion blur.