LFD-Net: Lightweight Feature-Interaction Dehazing Network for Real-Time Remote Sensing Tasks

Abstract

Currently, remote sensing equipments are evolving toward intelligence and integration, incorporating edge computing techniques to enable real-time responses. One of the key challenges in enhancing downstream decision-making capabilities is the preprocessing step of image dehazing. Existing dehazing methods usually suffer from steep computational costs with densely connected residual modules, as well as difficulties in maintaining visual quality. To tackle these problems, we designed a lightweight atmosphere scattering model based network structure to extract, fuse, and weight multiscale features. Our proposed LFD-Net demonstrates strong interpretability by exploiting the gated fusion module and attention mechanism to realize feature interactions between multilevel representations. The experimental results of LFD-Net on SOTS dataset reach an average frequency per second of 54.41, approximately eight times faster than seven most popular methods with equivalent metrics. After image dehazing by LFD-Net, the performance of object detection is significantly improved. The mean average precision when IoU = 0.5 ([email protected]) based on YOLOv5 is improved by 4.73% on DAIR-V2X dataset, which verified the practicability and adaptability of LFD-Net for real-time vision tasks.