Limitations of super resolution image reconstruction and how to overcome them for a single image

Super resolution image reconstruction (SRR) is a typical super resolution (SR) technology that has been researched with varying results. The SRR algorithm was initially proposed for still images. It uses many low-resolution images to reconstruct a high-resolution image. Unfortunately, in practice, we rarely have a sufficient number of low-resolution images for SRR to work. Usually, there is only one (or a few) blurry images. On the other hand, there is a need to improve blurry images in applications ranging from security and photo restoration to zooming functions and countless other examples related to the printing industry. Recently, SRR was extended to video sequences that have many similar frames that can be used as low-resolution images to reconstruct high-resolution frames. In normal SRR, one reconstructs a high-resolution image from low-resolution images sampled from one high-resolution image, but in the video application, the low-resolution video frames are not taken from higher resolution ones. This paper proposes a novel resolution improvement method that works without such a high- resolution image. Its algorithm is simple and can be applied to a single image and real-time video systems.