Deep Multi-Patch Hierarchical Network-based Visibility Restoration Model for Autonomous Vehicles

Clear visibility of the road is crucial for autonomous vehicles, but adverse weather conditions can significantly reduce road visibility, impacting their performance. To address this challenge, a visibility restoration model is essential for autonomous driving and navigation. This paper proposes a Lightweight Visibility Restoration Network (LVR-Net) designed to restore images with improved spatial and spectral information despite severe weather degradation. Initially, a Deep MultiPatch Hierarchical Network (DMPHN) is employed, but its computational demands hinder deployment on autonomous vehicles. To overcome this, a Modified Adaptive distributed Differential Evolution (MADE) optimization technique is applied to enhance the network's size, computational speed, and overall performance. A multi-objective fitness function based on Peak Signal-to-Noise Ratio (PSNR), memory size, and computational speed is formulated. Benchmark datasets are utilized to train, validate, and test the LVR-Net. Experimental results demonstrate that the proposed LVR-Net outperforms other competitive models across various performance metrics. Additionally, its compact size (48.25MB) and faster scene restoration make it highly suitable for deployment in autonomous vehicles.