Parallel implementation of multiframe blind deconvolution based on GPU

Multiframe blind deconvolution algorithm,which is one of the primary methods for restoring image,can enhance the resolving power of the adaptive optical images. However,the multiframe blind deconvolution algorithm takes the alternate minimization, optimize deconvolution methods to solve the images and the point spread functions (PSFs) of the objects, the arithmetic is complexed and time-consuming,it always needs several minutes even up to several quarters to solve the problem. Via developing the central processing unit-graphics processing unit (CPU-GPU) heterogeneous system architecture, through the combination of using library function and modulating the structure of the algorithm, we optimized the most time-consuming part, matrix convolution, and achieved the parallel methods of restoring image.Results show that for more than 16 frames of spatical target images with 256256 pixels,the speed-up ratio of 17 could be realized.This paper can provide a feasible scheme for real-time/quasi real-timeimage restoration.