FSDN: Image frequency and semantic decomposition network for image dehazing
Zongyang Tong, Mingyu Liu, Xin Jin · 2024
Image dehazing plays a crucial role in autonomous driving and outdoor surveillance. However, as haze affects different components of an image in various ways and degrees, existing methods treat the image as a singular input and overlook the need to decouple different components, leading to mutual interference during the enhancement of each component. Consequently, issues such as insufficient color restoration or blurred edges may arise. In this paper, we introduce a novel tri-branch network for Single Image Dehazing that independently extracts low-frequency, high-frequency, and semantic information from images using three distinct sub-networks. A meticulously designed fusion network is then employed to integrate the information from these three branches to produce the final dehazed image. To facilitate the training of such a complex network, we propose a two-stage training approach. Experimental results demonstrate that our approach achieves state-of-the-art (SOTA) performance.