GPU-based parallel algorithm for blind image restoration using midfrequency-based methods
文献类型：会议论文
| 作者 | Xie, Lang; Luo, Yi-Han; Bao, Qi-Liang |
| 出版日期 | 2013 |
| 会议名称 | Proceedings of SPIE: International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications |
| 会议日期 | 2013 |
| 卷号 | 8910 |
| 页码 | 89101R |
| 中文摘要 | 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. © 2013 SPIE. |
| 英文摘要 | 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. © 2013 SPIE. |
| 收录类别 | EI |
| 学科主题 | Filtration - Image reconstruction - Optical transfer function - Parallel algorithms - Parallel architectures |
| 语种 | 英语 |
| ISSN号 | 0277786X |
| 源URL | [http://ir.ioe.ac.cn/handle/181551/7401]  |
| 专题 | 光电技术研究所_光电工程总体研究室（一室） |
| 作者单位 | Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China |
| 推荐引用方式 GB/T 7714 | Xie, Lang,Luo, Yi-Han,Bao, Qi-Liang. GPU-based parallel algorithm for blind image restoration using midfrequency-based methods[C]. 见:Proceedings of SPIE: International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications. 2013. |
入库方式： OAI收割
来源：光电技术研究所
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