A Real-Time Learning-Based Super-Resolution System on FPGA

Abstract

This paper proposes a real-time super-resolution (SR) system. The proposed system performs a fast SR algorithm that generates a high-resolution image from a low-resolution image using direct regression functions with an up-scaling factor of 2. This algorithm contained two processes: feature learning and SR image prediction. The feature learning stage is performed offline, in which several regression functions were trained. The SR image prediction stage is implemented on the proposed system to generate high-resolution image patches. The system implemented on a Xilinx Virtex 7 field-programmable gate array achieves output resolution of [Formula: see text] (UHD) at 85 fps and 700Mpixels/s throughput. Structure similarity (SSIM) is measured for image quality. Experimental results show that the proposed system provides high image quality for real-time applications. And the proposed system possesses high scalability for resolution.