Fast MAP-Based Super Resolution Algorithm Using Multilevel Interpolation and Reconstruction

Abstract

Super-resolution (SR) image reconstruction can produce a higher resolution digital image from multiple low-resolution (LR) photographs. Many applications require high resolution images, including medical imaging, satellite imaging, and video applications. Recovering lost details from down-sampled images is the main challenge of super resolution techniques. However, most researches do not take computational complexity into consideration. Therefore, this paper presents a new fast maximum a posteriori (MAP)-based SR image reconstruction method, it is based on the multilevel algorithm. In particular, this work focuses on the case of the input LR images that are not enough for analysis. A two-step interpolation process is proposed to increase the quality of the constructed SR image. Experimental results show that the proposed algorithm can simultaneously reduce blocking artifacts caused by a lack of LR images and computational complexity.