Year: 2025
Type: article
Abstract: In response to the inherent limitations of uncooled infrared imaging devices and the image degradation caused by UAV (Unmanned Aerial Vehicle) platform motion, resulting in low contrast and blurred details, a novel single-image blind deblurring and enhancement network is proposed for UAV infrared imagery. This network achieves global blind deblurring and local feature enhancement, laying a foundation for subsequent high-level vision tasks. The proposed architecture comprises three key modules: feature extraction, feature fusion, and simulated diffusion. Furthermore, a region-specific pixel loss is introduced to strengthen local feature perception, while a progressive training strategy is adopted to optimize model performance. Experimental results on public infrared datasets demonstrate that the presented method outperforms state-of-the-art methods HCTIRdeblur, reducing parameter count by 18.4%, improving PSNR by 10.7%, and decreasing edge inference time by 25.6%. This work addresses critical challenges in UAV infrared image processing and offers a promising solution for real-world applications.
Source: Remote Sensing
DOI: 10.3390/rs17203446
Institution: National University of Defense Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4415230261