| 内容提要: |
GPU-based general-purpose computing is a new branch of modern parallel computing, so the study of parallel
algorithms specially designed for GPU hardware architecture is of great significance. In order to solve the problem of
high computational complexity and poor real-time performance in blind image restoration, the midfrequency-based
algorithm for blind image restoration was analyzed and improved in this paper. Furthermore, a midfrequency-based
filtering method is also used to restore the image hardly with any recursion or iteration. Combining the algorithm with
data intensiveness, data parallel computing and GPU execution model of single instruction and multiple threads, a new
parallel midfrequency-based algorithm for blind image restoration is proposed in this paper, which is suitable for stream
computing of GPU. In this algorithm, the GPU is utilized to accelerate the estimation of class-G point spread functions
and midfrequency-based filtering. Aiming at better management of the GPU threads, the threads in a grid are scheduled
according to the decomposition of the filtering data in frequency domain after the optimization of data access and the
communication between the host and the device. The kernel parallelism structure is determined by the decomposition of
the filtering data to ensure the transmission rate to get around the memory bandwidth limitation. The results show that,
with the new algorithm, the operational speed is significantly increased and the real-time performance of image
restoration is effectively improved, especially for high-resolution images. |