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.
Blind image restoration (BIR) is a useful tool in image processing but too time-consuming to be applied. In order to truly achieve real-time BIR, an ultrafast method for BIR is proposed based on a new concept called the midfrequency region (MR) of an image, which can be used to estimate the point spread function (PSF) and rapidly and accurately restore the image. Also, new MR-based Wiener filtering is derived to accomplish BIR more quickly. Then, using the PSFs of class G that fit numerous imaging systems, a new MR-based algorithm for real-time BIR is derived via independent component analysis and genetic algorithms. Experiments prove that the algorithm is quite effective, speedy, and stable and can truly accomplish real-time image deblurring in on-line systems.
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