A novel and efficient image dehazing technique for Advanced Driver Assistance Systems

Abstract

Advanced Driver Assistance Systems (ADAS) rely on clear visual input to ensure safe and accurate driving decisions. However, atmospheric conditions like haze and fog can significantly reduce image visibility and clarity. This paper presents an efficient and lightweight image dehazing method specifically designed for ADAS applications. The proposed approach is based on two core modules: Depth Refinement Transmission Rate Estimation (DRTRE) and Distributed AirLight Estimation (DALE). Unlike deep learning-based techniques, our method does not require any training data or neural networks, making it well-suited for real-time hardware implementation. DRTRE estimates scene depth using the saturation and value components of the image and refines the transmission rate through adaptive thresholding and calibration. DALE improves the estimation of AirLight by analyzing spatially distributed depth values to handle non-uniform haze. Together, these modules restore clear images while minimizing computational overhead. Experimental results show that the proposed dehazing method achieves an average PSNR improvement of up to 18.19% and MSE reduction of approximately 33.80% compared to existing methods. It also demonstrates a consistent improvement in SSIM, with gains of up to 11.63%, indicating enhanced structural fidelity. Furthermore, the method improves the Comprehensive Performance Metric (CPM) by up to 4.07 times and reduces the Naturalness Image Quality Evaluator (NIQE) by as much as 17.17%, confirming superior perceptual and quantitative performance. The complete system is implemented in Verilog Hardware Description Language (HDL) and synthesized on a Xilinx Zynq-7000 series Field Programmable Gate Array (FPGA). The proposed architecture demonstrates substantial hardware efficiency, achieving reductions of up to 98.3% in logic elements, 54.4% in memory registers, and 61.9% in line buffer usage compared to existing designs.