Bayes' rule based multi-frame blind convolution super-resolution reconstruction method and device

Abstract

The invention discloses a Bayes' rule based multi-frame blind convolution super-resolution reconstruction method and device. The method includes steps of acquiring an interest area of a reference image and a matching area of a target image subjected to radiation correction through an image quality evaluation and frame selection algorithm for an image sequence of one scene; acquiring the radiancy and an accurate geometric distortion parameter through an image registration algorithm executed on the matching area of the target area subjected to radiation correction; acquiring a point diffusion function of image super-resolution restoration through execution of a multi-frame blind deconvolution image restoration algorithm on the accurate geometric distortion parameter; acquiring a super-resolution reconstruction image through execution of a maximum posterior super-resolution reconstruction algorithm on the radiancy and the point diffusion function of image super-resolution restoration. According to the invention, problems of insufficient consideration of point diffusion function, motion blur, image structure information, sparsity and the like of a traditional general algorithm are solved, automatic estimation on the system point diffusion function and multi-frame image registration parameters is performed, and the image resolution is improved.

Description

technical field [0001] The invention belongs to the technical field of super-resolution reconstruction, in particular to a multi-frame blind convolution super-resolution reconstruction method and device based on Bayesian rule. Background technique [0002] Super-resolution reconstruction technology (Super-Resolution, SR) is a software-based image post-processing technology, which can use multiple low-resolution images of the same scene to reconstruct a high-resolution image, which is the key to improving the image spatial resolution. Another effective way. [0003] At present, a variety of classic super-resolution algorithms commonly used in the world include: Iterative BackProjection (IBP), Projection Onto Convex (POCS), Maximum Likelihood Estimation (Maximum Likelihood, ML), Maximum Posterior Experimental estimation method (Maximum A Posterior, MAP) and hybrid ML / MAP / POCS method, etc. The observation models of these methods are mostly based on the shift characteristics o...

AI Technical Summary

Problems solved by technology

[0004] The technical problem solved by the present invention is: to overcome the deficiencies of the prior art, to provide a multi-frame blind convolution super-resolution reconstruction method and device based on Bayesian criterion, to solve the problem of traditional general algorithm on point spread function, motion blur and The problem of image structure information and sparsity, automatic estimation of system point spread function and multi-frame image registration parameters, improved image resolution

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