A Lightweight Dual-Branch Image Dehazing Network based on Associative Learning

Abstract

Haze degrades the clarity, contrast, and details of images, resulting in a decrease in image quality. Image dehazing provides a means to obtain clearer and more accurate image information. Traditional methods for haze removal typically rely on manually designed features and models, limiting their performance in complex scenes. In recent years, the rapid advancement of deep learning has offered new insights into addressing the image dehazing problem. This paper proposes a lightweight dual-branch image dehazing network based on associative learning (LDANet). The network consists of a lightweight dehazing sub-network (LDSN) and a lightweight image enhancement subnetwork (LESN). To reduce computational and parameter complexity, the Tied Block Convolution (TBC) is employed, allowing for parameter sharing among components. Lastly, through associative learning, their distinctive features are mapped. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of our approach in qualitative comparisons and quantitative evaluations compared to other state-of-the-art methods. Our method holds significant practical value for real-world image dehazing scenarios.