Research On The Super-Resolution Imaging And Their Correlative Technologies Based On The Image Sequence With Displacement

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 It breaks through the resolution limit of image acquisition equipment and can achieve data fusion on subpixel level. Image SR imaging 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.This paper around the super-resolution imaging technique of image noise reduction, image registration and super-resolution image reconstruction, and other related technologies, include the following parts :(1) After the analysis of the common image denoising, median filtering algorithm is focused on, by which the salt and pepper noise can be effectively removed, compared with the traditional median filtering algorithm, adaptive filtering algorithms and literature referred to the threshold value of the median filtering algorithm is good or bad.(2)Based on the mutual information of the image data for the study, at first, two optimization algorithms, the Powell algorithm and the particle swarm optimization (PSO), are compared. As Powell algorithm to calculate speed and the initial value, so, Powell optimization algorithm combining wavelet decomposition pyramid registration method is put forward, and the algorithm's computational speed and accuracy are improved. At last a PSO and Powell Hybrid algorithm mixed with improved wavelet pyramids of the optimization method is proposed.(3)Without the consideration for noise and pixel displacement of the known circumstances, based on mutual displacement of the inversion image sequence analysis and image reconstruction algorithm to be improved, improved the original image two times resolution; In considering the noise conditions, sample raised image of the point spread function of the estimated based on the Wiener filter image reconstruction methods. Experiments based on the BP neural network algorithm for image reconstruction methods, together with the results of the reconstruction were compared and analyzed.