Research On Key Technologies Of Super-resolution Image Reconstruction

Image super resolution (SR) processing is the technology to reconstruct high resolution and high quality images from a group of warped, blurred and noised low resolution (LR) images about the same scene. It breaks through the resolution limit of image acquisition equipment, uses complement information in multi-frame and can achieve data fusion on pixel level. Image SR processing has proved to be useful in many practical applications, such as remote sensing, military detection, HDTV, medical imaging, machine vision and public security, etc.Super resolution algorithms include frequency-domain approach and spatial-domain approach. Frequency domain approach can only deal with image sequences that only translational motions are allowed. Spatial methods which are the major research directions have better adaptability and performance by using general observation models. Spatial methods include the iterative back-projection (IBP) method, stochastic method (MAP), projection on convex set (POCS) method, hybrid reconstruction method and adaptive filter method, etc. This paper introduces research of image super resolution reconstruction technologies all-around and studies three areas in-depth: reference frame reconstruction; motion estimation and conjugate gradient image reconstruction that used motion-estimated information; quality assessment of image reconstruction.Traditional image interpolation algorithms are study in this paper. For reference frame reconstruction, all-phase interpolation algorithms are put forward. Base on two factors of an edge profile: the gray value across the edge orientation is sharp and the gray value along the edge orientation is smooth, this paper proposes modified all-phase interpolation algorithm.Motion estimation is an important technique in super resolution problems, in SR reconstruction motion estimation is used to project the observation frames onto the reference frame. The accuracy of motion estimation has great effect to the reconstruction results. The key to the SR image reconstruction is the accurate knowledge of the sub-pixel motion information of the neighbor frames, this paper put forward motion estimation algorithm base on frame differences. Compared with traditional block matching algorithm (BMA), the results of this algorithm are accuracy especially in edge area. Considering the benefit and defect of BMA and optical flow method, this paper studies combined method, gets weight information, and uses conjugate gradient to reconstruct SR image.At present, there's no uniform assessment method on the quality of SR reconstruction image. Considering the characteristics of SR image, the assessment method based on edge block and the relative of LR image edge is proposed. Edge areas are important in SR reconstruction, especially for human being's visual system. Edge emphasized method is studied based on the human visual sensation. In this paper, these algorithms are emulated and the results are better.