Super-resolution (SR) can greatly promote the development of edge electro-optical (EO) devices. However, most existing SR models struggle to simultaneously achieve effective thermal reconstruction and real-time inference on EO devices with limited computing resources. To address these issues, this work proposes a novel fast thermal SR model (EdgeSR) for edge EO devices. Specifically, reparameterized scale-integrated convolutions (RepSConv) are proposed to deeply explore high-frequency features, incorporating multi-scale information and enhancing the scale-awareness of the backbone during the training phase. Furthermore, an interactive reparameterization module (IRM), combining historical high-frequency with low-frequency information, is introduced to guide the extraction of high-frequency features, ultimately boosting the high-quality reconstruction of thermal images. Edge EO deployment-oriented reparameterization (EEDR) is designed to reparameterize all modules into standard convolutions that are hardware-friendly for edge EO devices, onboard real-time inference. Additionally, a new benchmark for thermal SR on cityscapes (CS-TSR) is built. The experimental results on this benchmark show that, compared to state-of-the-art (SOTA) lightweight SR networks, EdgeSR delivers superior reconstruction quality and faster inference speed on edge EO devices. In real-world applications, EdgeSR exhibits robust performance on edge EO devices, making it suitable for real-world deployment. The code and demo is available at https://github.com/vision4robotics/EdgeSR.
Please install related libraries:
pip install -r requirementsThe checkpoint is available here, password: djp8.
To test on CS-TSR benchmarks, you need to download them from the following links:
- CS-TSR, password: djp8
python test.pyThe testing result will be saved in the result directory
The code is based on SRFormer and ETDS. We would like to express our sincere thanks to the contributors.
If you have any questions, please contact Ziyu Lu at [email protected].