Research On The Algorithms Of Super-resolution Image Fusion In Spatial Domain

Super-resolution(SR) image fusion is the technology of reconstructing a frame of image with high resolution from a group of warped, blurred and noised low-resolution(LR) images or video sequence about the same scene. The goals of this technology are to break through the resolution limitation of the imaging hardware facilities, to make up the loss of spatial resolution during the acquisition and transmission of images by the help of the data fusion of complementary information between multi-frame images, and then to reconstruct images with higher resolution. It can not only improve the visual effect of image, but also be convenient and beneficial for computer to analyze, process, recognize and understand the image. So far, the technology of Super-resolution image fusion has been extensively applied to remote sensing, military, public security, computer vision, medical imaging, multi-media electronic consuming, image compression and so on. Therefore, this technology has great prospect with significant applicational value.This thesis firstly introduces the developmental process of Super-resolution image fusion as well as all kinds of algorithms basing on both frequency domain and spatial domain. Two of the most successful algorithms, namely, Projection onto Convex Sets(POCS) and Maximum A Posteriori(MAP), will be studied in depth basing on the full analyses and comparisons of all these algorithms.Two of the key steps of this technology are the establishment of observation model and image registration (motion estimation ). Thus, this thesis secondly sets up the observation model, and then provides a detailed introduction to the principle, classification, and manipulation of image registration. This thesis adopts a pyramid structure image registration method which is based on the optical flow, since it can provide a good registration for big shifted image.Thirdly, this thesis focuses on the study of the principle, realization and steps of the Projection onto Convex Sets methods. In the light of the weakness of the traditional method, this thesis improves the Point Spread Function (PSF) of the imaging system. Meanwhile, this thesis adds line process as a priori to set up several convex constraint sets. We also simplify the line process for practical realization. The efficiency of this algorithm can be recognized from the experimental results which are attached at the end of this thesis.This thesis ends with the study of the Maximum A Posteriori(MAP) method with...