Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
Authors: Jaeha Kim, Junghun Oh, Kyoung Mu Lee

In real-world scenarios image recognition tasks such as semantic segmentation and object detection often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image super-resolution (SR) is one of the promising solutions for addressing the challenges. However due to the ill-posed property of SR it is challenging for typical SR methods to restore task-relevant high-frequency contents which may dilute the advantage of utilizing the SR method. Therefore in this paper we propose Super-Resolution for Image Recognition (SR4IR) that effectively guides the generation of SR images beneficial to achieving satisfactory image recognition performance when processing LR images. The critical component of our SR4IR is the task-driven perceptual (TDP) loss that enables the SR network to acquire task-specific knowledge from a network tailored for a specific task. Moreover we propose a cross-quality patch mix and an alternate training framework that significantly enhances the efficacy of the TDP loss by addressing potential problems when employing the TDP loss. Through extensive experiments we demonstrate that our SR4IR achieves outstanding task performance by generating SR images useful for a specific image recognition task including semantic segmentation object detection and image classification. The implementation code is available at https://github.com/JaehaKim97/SR4IR.