Superresolution of real image sequence

Image superresolution has attracted substantial attention in the image processing community in recent years. Valuable techniques have been developed, and practical results have been obtained. However, in much of the literature, successes are frequently demonstrated in synthetic simulations, which limit a technique's practical use.

This thesis will develop a technique to superresolve a real image sequence. This technique consists of three portions: system blur and noise removal, image registration, and sequence combination. First, the system blur and noise removal is achieved by a new approach of Point Spread Function (PSF) estimation. This approach is easy, cost-effective, and accurate compared to traditional methods. Then, image registration is performed, based on inserted fiducials. Translational shifts, rotation, scaling, and geometric distortions can be handled by this method. Finally, three different framecombining algorithms are implemented and compared.

These techniques are demonstrated on an image sequence taken by a Canon EOS D30 digital camera. Quarter pixel superresolved images with sharper edges are obtained. The results confirm the effectiveness of these techniques. Analyses are done in terms of performance and implementation complexity.