Journal Article

MWR-Net: An Edge-Oriented Lightweight Framework for Image Restoration in Single-Lens Infrared Computational Imaging

Yang, Guishuo, Wang, Zhanshan, Dun, Xiong, Xing, Yujie, Wang, Xuquan, Qian, Xuanyu, Cheng, Xinbin

2025

Overview

Infrared video imaging is an cornerstone technology for environmental perception, particularly in drone-based remote sensing applications such as disaster assessment and infrastructure inspection. Conventional systems, however, rely on bulky optical architectures that limit deployment on lightweight aerial platforms. Computational imaging offers a promising alternative by integrating optical encoding with algorithmic reconstruction, enabling compact hardware while maintaining imaging performance comparable to sophisticated multi-lens systems. Nonetheless, achieving real-time video-rate computational image restoration on resource-constrained unmanned aerial vehicles (UAVs) remains a critical challenge. To address this, we propose Mobile Wavelet Restoration-Net (MWR-Net), a lightweight deep learning framework tailored for real-time infrared image restoration. Built on a MobileNetV4 backbone, MWR-Net leverages depthwise separable convolutions and an optimized downsampling scheme to minimize parameters and computational overhead. A novel wavelet-domain loss enhances high-frequency detail recovery, while the modulation transfer function (MTF) is adopted as an optics-aware evaluation metric. With only 666.37 K parameters and 6.17 G MACs, MWR-Net achieves a PSNR of 37.10 dB and an SSIM of 0.964 on a custom dataset, outperforming a pruned U-Net baseline. Deployed on an RK3588 chip, it runs at 42 FPS. These results demonstrate MWR-Net’s potential as an efficient and practical solution for UAV-based infrared sensing applications.

• Publisher: MDPI AG
• Subject: Algorithms / Architecture / Border patrol / Co-design / Computer applications / computional imaging / Deep learning / Edge computing / Efficiency / Environmental perception / Image processing / Image reconstruction / Image restoration / Infrared imagery / Infrared imaging / Infrared imaging systems / infrared video imaging / Lenses / lightweight deep learning / mobilenet / Modulation transfer function / Neural networks / Optics / Parameters / Real time / real-time image restoration / Remote sensing / Technology application / Unmanned aerial vehicles