Study Of The Super-Resolution Reconstruction With Image Sequence Of Simultaneous Scene

Image super-resolution (SR) processing is the technology to reconstruct high resolution and high quality image(s) from a group of warped, blurred and noised low resolution images about the same scene. It breaks through the resolution limit of image acquisition equipment and can achieve data fusion on subpixel level. Image SR processing has proved to be useful in many practical applications, such as remote sensing, military detection, medical imaging, machine vision and public security, etc. It can greatly improve spatial resolution and enhance image quality. So it is beneficial for computer to process, analyze and understand the image.The main contents and contributions of this dissertation are as follows: 1 .A comprehensive review of the basic theories and techniques of super-resolution is addressed . The process of acquiring images and the model are analyzed briefly and the SR ability and feasibility are discussed.2. Based on the method of grey level and feature of image, smooth geometric distortion and high accuracy feature point extraction and matching algorithm are discussed in digital photogrammetry, and the way of block matching and optical flow field is described to improve the efficiency of image registration on motion estimation between images, which lays a good foundation for images super-resolution.3. Based on the multiresolution analysis with orthonormal wavelets, the wavelet super-resolution algorithm for the more general camera motion model is analyzed. The implementation of this algorithm to the super-resolution for remote sensing image is a great progress4. The basic theories and procedure of the MAP method and POCS method in spatial domain are summarized. These methods are applied for the super-resolution for images successfully and better super-resolution reconstruction results are obtained. It is a progress made in applications for image super-resolution.5. The theories and procedures of super-resolution via alias removal are analyzed, the correctness and feasibility of this method are proved in theory, and the method compared with spatial field on the capability.6. In conjugate gradient optimization algorithm, the continuous and digital models of an imaging system are defined to explain image acquisition, the image registration algorithm and the conjugate gradient reconstruction algorithm are designed. Experiment results show that the conjugate gradient reconstruction algorithm is robust, rapid convergent, and memory saved. This algorithm is prone to be extended and make it possible that multiple sensor data fusion constructs high resolution images.7. A series of experiments on image reconstruction are given in the thesis and some worthy conclusions are obtained. The experiment and calculation results show that the reconstruction schemes and evaluation criteria are effectual.