Research On Restoration Algorithm From Sequence Images For Scene Supervision

In recent years, video scene monitoring system has been widely used in various fields. However, due to some reasons such as weather, lighting, camera movement, signal transmission and restrictions of imaging system itself, the clarity of the images has been decreased, and the quality of images has been inevitably reduced seriously. Images may be not clean and difficult to be seen in the details; More seriously, images are not clear, and they even can't be recognized in general. All these defects decrease the functions of video scene monitoring system greatly. So research of restoration algorithms from sequence images is important for video scene monitoring system.Based on a lot of information and a variety of experimental methods, this thesis has combined the image registration method and the super-resolution reconstruction algorithm in accordance with the characteristics that the objects in adjacent frames turned up repeatedly, changed little and made up for each other in the sequential images, and provided a new method to clear the image sequence against the global campaign. Through processing the sequence images that are not clean and have noise, it can generate a better high-resolution image than the original images.This thesis has analyzed the imaging model and the issues that refer to the reconstruction of the image sequence deeply, starting from the main factors that video scene monitoring system affect the image quality in. Against the image sequence of the global campaign, it has introducted the mutual information as the measure of the similar degree of the image registration, combined with the general ideas of Powell optimization, and discussed the registration method based on the two ones. On this basis, it has analysed the super-resolution reconstruction algorithms of the POCS (convex projection), improved the estimation of the initial value against the characteristics that the convex projection algorithm has the slow convergence and the weak capacity to eliminate noise, and adopted an approach of median integration to estimate the initial estimate frame that has related information of the image sequence, and then enhanced the image clarity and the effects of eliminating noise. Finally, the software platform that is programmed by Matlab to verify the algorithm has improved the efficiency of research and tested the standard images and the real monitor images respectively. Through the analysis of the results, the method is proved feasible and effective.