Objective image quality assessment of multiframe super-resolution methods

Super-resolution (SR) represents a class of signal processing methods allowing to create a high resolution image (HR) from several low resolution images (LR) of the same scene. Therefore, high spatial frequency information can be recovered. Applications may include but are not limited to HDTV, biological imaging, surveillance, forensic investigation. In this paper, a survey of SR methods is provided with focus on the non-uniform interpolation SR approach because of its lower computational demand. Based on this survey eight SR reconstruction algorithms were implemented. Performance of these algorithms was evaluated by means of objective image quality criteria PSNR, MSSIM and computational complexity to determine the most suitable algorithm for real video applications. The algorithm should be reasonably computationally efficient to process a large number of color images and achieve good image quality for input videos with various characteristics. This algorithm has been successfully applied and its performance illustrated on examples of real video sequences from different domains.