The Image Super-resolution Reconstruction Based On Markov Random Field

Super-resolution Image reconstruction technic is an important branch of image fusion area, which can estimate one or more higher resolution distortionless images by a series of Low-Resolution distortion images, and eliminate Additive Noise and blur which is produced by limited detector dimension and optics to obtain more image information and details.There are two sorts of super-resolution Image reconstruction algorithm: frequency domain algorithm and spatial domain algorithm. Frequency domain algorithm comes up prior to spatial domain algorithm, but can simply deal with overall displacement image sequences; Spatial domain algorithm is the main investigation at present, which is more agile, that including: Iterative Back Projection (IBP) algorithm, Projection onto Convex Sets (POCS) algorithm and the Maximum a posteriori(MAP) algorithm, but these algorithms have the defects of slow rate of convergence and large amount of calculations, and can not satisfy the requirement of the timeliness of system in practice, so that Super-resolution Image reconstruction is always a difficulty in the area of image treatment.This thesis lucubrated super-resolution Image algorithm, designed exactitude image model On the premise of ensuring the effect of the super-resolution Image reconstruction, and brought up a super-resolution Image reconstruction algorithm which was based on Markov random field. At first, the algorithm adopted an improved three step search algorithm to carry out motion estimation for image. The algorithm gives attention to two conditions of large and small motion estimation at the same time. A good motion estimation effect would be come out even in the condition of small motion estimation. Three step search algorithm combines the characteristic of the motion image optimum point always distributes around the zero vector, which speeded up the progress of motion estimation and reduced the amount of calculation of super-resolution Image reconstruction. Second, the algorithm adopted bilinear interpolation algorithm, and increased 2 times of image resolution. At last, using Potts-Strauss model as the priori probability density function, and realized super-resolution Image reconstruction by Iterated Condition Mode algorithm. The result of simulation experiment has proved the validity of the way, and has overcome the shortcoming of slow rate of convergence and large amount of calculations which belong to kinds of algorithm at present. It is a kind of super-resolution Image reconstruction of high efficiency that would be valuable in practice.