Acceleration techniques and evaluation on multi-core CPU, GPU and FPGA for image processing and super-resolution

Abstract

Super-Resolution (SR) techniques constitute a key element in image applications, which need high- resolution reconstruction while in the worst case only a single low-resolution observation is available. SR techniques involve computationally demanding processes and thus researchers are currently focusing on SR performance acceleration. Aiming at improving the SR performance, the current paper builds up on the characteristics of the L-SEABI Super-Resolution (SR) method to introduce parallelization techniques for GPUs and FPGAs. The proposed techniques accelerate GPU reconstruction of Ultra-High Definition content, by achieving three (3x) times faster than the real-time performance on mid-range and previous generation devices and at least nine times (9x) faster than the real-time performance on high-end GPUs. The FPGA design leads to a scalable architecture performing four (4x) times faster than the real-time on low-end Xilinx Virtex 5 devices and sixty-nine times (69x) faster than the real-time on the Virtex 2000t. Moreover, we confirm the benefits of the proposed acceleration techniques by employing them on a different category of image-processing algorithms: on window-based Disparity functions, for which the proposed GPU technique shows an improvement over the CPU performance ranging from 14 times (14x) to 64 times (64x) while the proposed FPGA architecture provides 29x acceleration.