Task-Driven Super-Resolution

(Under consideration at Computer Vision and Image Understanding))

Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita

Abstract

We consider how image super resolution (SR) can contribute to an object detection task in low-resolution images. Intuitively, SR gives a positive impact on the object detection task. While several previous works demonstrated that this intuition is correct, SR and detector are optimized independently in these works. This paper proposes a novel framework to train a deep neural network where the SR sub-network explicitly incorporates a detection loss in its training objective, via a tradeoff with a traditional detection loss. This end-to-end training procedure allows us to train SR preprocessing for any differentiable detector. We demonstrate that our task-driven SR consistently and significantly improves accuracy of an object detector on low-resolution images for a variety of conditions and scaling factors.

Manuscript

Code

Citation

Muhammad Haris, Greg Shakhnarovich, and Norimichi Ukita. "Task-Driven Super Resolution: Object Detection in Low-resolution Images." arXiv preprint arXiv:1803.11316 (2018).