  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.
