Map Based Research Of Super-Resolution Image Reconstruction

Generally speaking, super-resolution technology includes super-resolution and resolution enhancement. Because of its perfect future of application, more and more image processing experts has paid attention to image super-resolution technology recently. Image restoration is focus on the reason of all types of distortions in the imaging system, and highly restores the truth image. Super-resolution restoration (reconstruction) is based on image restoration and reconstruction. In tradition single image restoration only a single input image is available for processing, which results in the limited capability of resolution improvements. Thus a new technique, super-resolution is required. On the other hand, a low cost and easy used approach is indispensable to obtain high quality images in many practical, including safety monitoring, health diagnosis and military surveillance. The technique of image super-resolution restoration makes it possible that high resolution images could be restored from low resolution images recorded by low resolution sensors. Super-resolution image restoration addresses the problem of producing super-resolution still image from several images, which contain additional similar, but not identical information. The additional information make it possible that construct an higher resolution image from original data.This dissertation introduces mathematic model, reviews reconstruction algorithms, and carries on analysis and research. Finally some significant schemes are proposed. In general, the main ideas are included as follows:1 A new technique for selection parameter is proposed in this paper.2 Exist in the method to single width image, design one test Probability estimate(MAP) under the frame because of being most heavy ,utilize the high image of the array image of many frames to construct the method again.3 Considering of common moving image, this thesis Puts Forward to an improved Unscented kalman filter method.