Abstract
The traditional super-resolution image reconstruction methods for optimization and implementation are designed for common processor (CPU). According to the parallel computing capability of GPU-CUDA, a fast super-resolution image reconstruction method is presented based on GPU-CUDA. Additionally, we proposed the MAP framework that can allocate sub-pixel displacement information of low-resolution images to the unified super-resolution image grid. On the basis of the parallel architecture and hardware characteristic of GPU, the acceleration method use CUDA programmable parallel framework to optimize the data storage structure, improve the efficiency of data access and reduce the complexity of the algorithm. The experiment expressed that we could get an over ten times speed effect by this method than traditional super-resolution image reconstruction methods.
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Song, Z., Chen, Z., Shi, R. (2015). Fast MAP-Based Super-Resolution Image Reconstruction on GPU-CUDA. In: Bian, F., Xie, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2014. Communications in Computer and Information Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45737-5_17
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DOI: https://doi.org/10.1007/978-3-662-45737-5_17
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