Accelerate Single Image Super-Resolution Using Object Detection Process

Image Super-Resolution (SR) research has achieved great success with powerful neural networks. The deeper networks with more parameters improve the restoration quality but add the computation complexity, which means more inference time would be cost, hindering image SR from practical usage. Noting the spatial distribution of the objects or things in images, a two-stage local objects SR system is proposed, which consists of two modules, the object detection module and the SR module. Firstly, You Only Look Once (YOLO), which is efficient in generic object detection tasks, is selected to detect the input images for obtaining objects of interest, then put them into the SR module and output corresponding High-Resolution (HR) sub-images. The computational power consumption of image SR is optimized by reducing the resolution of input images. In addition, we establish a dataset, TrafficSign500, for our experiment. Finally, the performance of the proposed system is evaluated under several State-Of-The-Art (SOTA) YOLOv5 and SISR models. Results show that our system can achieve a tremendous computation improvement in image SR.