Frequency Domain Perception Filtering for Road Night Vision Image Enhancement in Edge-Cloud Scenario

This paper centers on edge-cloud-based road image enhancement under low-light nighttime conditions, with a detailed analysis of the frequency-domain characteristics of nighttime road surface images. Currently, deep learning-based night vision image enhancement methods are confronted with the extreme environment of data storms. To address this, this paper adopts an image enhancement scheme oriented toward edge node filtering. To begin with, the original image is transformed from the spatial domain to the frequency domain via the Fourier transform. Drawing on the frequency-domain attributes specific to nighttime road images, a frequency-domain perceptual filter is tailored, and its filtering parameters are dynamically adjusted-this enables the selective enhancement of low-frequency background contours and high-frequency fine details, while also achieving moderate noise suppression. Subsequently, the inverse Fourier transform is applied to convert the processed image back from the frequency domain to the spatial domain, yielding an initially enhanced nighttime road surface image. Following this, histogram matching is employed to further boost the contrast of the nighttime image. Finally, color balance correction is implemented to optimize the color rendering of the filtered image. Experimental results show that the frequency-domain perceptual filtering algorithm based on Fourier transform adopted in this paper can not only effectively improve image contrast and highlight road surface texture and color information, but also meet the lightweight deployment requirements of edge nodes and can be well adapted to the hardware constraints of edge system environments.