Wavelet-based physically guided normalization network for real-time traffic dehazing

Abstract

Single image Dehazing is a pressing task in everyday life, with deep learning having facilitated numerous research advancements. However, the field of image Dehazing is currently encountering a bottleneck. We can identify two primary reasons for the difficulty in further enhancing Dehazing quality. First, Convolutional Neural Networks (CNNs) struggle to capture long-range dependencies. Second, haze causes pixels that are similar in haze-free images to diverge in appearance. To address these challenges simultaneously, we propose a Wavelet-Based Physically Guided Normalization Dehazing Network (WBPGNDN). Specifically, we introduce a physically guided Normalization designed to restore the similarity of pixels as seen in haze-free images. Additionally, we utilize Wavelet Decomposition to seize long-range dependencies. While traditional methods typically apply wavelet decomposition in the image domain, we instead implement it in the feature domain. Experiments on both real and simulated hazy images demonstrate the Dehazing efficacy of our proposed method. The extensive results indicate that our approach matches or surpasses state-of-the-art methods, yielding high-quality visual outcomes and effectively addressing the limitations of existing methods.