BiDSRS+: Resource Efficient Reconfigurable Real Time Bidirectional Super Resolution System for FPGAs

Abstract

Super-resolution (SR) systems represent a rapidly advancing area within Information and Communication Technology (ICT) due to their significant applications in computer vision and visual communication. Integrating SR systems with Deep Neural Networks (DNNs) is a widely adopted method for leveraging faster and improved image reconstruction. However, the real-time computational demands, extensive energy overhead and the huge memory footprints associated with DNN-based SR systems limit their throughput and scalability. Field-programmable gate arrays (FPGAs) present a viable and promising solution for exploring the structure and architecture of SR systems due to their reconfigurable nature and parallel computing capabilities. The existing FPGA-based solutions can effectively reduce the computational latency in SR systems, they often result in higher resource and energy consumption. Besides, the traditional SR techniques generally focus on either upscaling or downscaling images or videos without offering any scaling reconfigurability. To address these limitations, this paper introduces BiDSRS+, a novel FPGA based resource-efficient and reconfigurable real-time SR system using modified bicubic interpolation method. In addition, BiDSRS+ supports both upscaling and downscaling of images and videos, enhancing its versatility. Evaluations conducted on the Xilinx ZCU 102 FPGA board reveal substantial resource savings, with reductions of 44x LUT, 31x BRAM, and 35x DSP utilization compared to state-of-the-art DNN-based SR systems, albeit with a trade-off in throughput of 0.5x. Furthermore, when compared to leading algorithm-based SR systems, BiDSRS+ achieves reductions of 5.8x LUT, 1.75x BRAM, and 2.3x Power consumption, without compromising the throughput. Due to its high resource efficiency and reconfigurability with a throughput of 4K@60 FPS, BiDSRS+ offers significant advantages in promoting sustainable and energy-efficient green video communication.