A Parallel Genetic Algorithm for Image Super-resolution Restoration
基于并行遗传算法的图像超分辨率复原

The technique of image super-resolution restoration makes it possible that high resolution images could be restored from low resolution images recorded by low resolution sensors. super-resolution restoration algorithms may be divided into two classes, particularly frequency domain and spatial domain. All frequency domain approaches made use of the aliasing effect; spatial domain algorithms there are mainly three approaches, i.e. Iterative Backward Projecting(IBP), Projection Onto Convex Sets(POCS) and Bayesian methods. In this paper, a parallel genetic framework algorithm for image (sequence) super-resolution restoration is presented . The parallelism of the real-valued genetic algorithm based on the island model enables better integration of the information of the multiple frame images. Especially with the iterative method of other super-resolution algorithms being the mutation operator, the convergence of the genetic searching in the solution space is fast. The experiments demonstrate that the proposed algorithm is efficient and applicable.