# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 1 Introduction  
Accurate perception of outdoor environments is essential for a wide range of computer vision applications, including autonomous driving, video surveillance, and drone navigation. However, atmospheric phenomena such as haze can severely impair image visibility, degrading the performance of downstream tasks such as object detection and tracking. Consequently, image dehazing techniques have become critical to ensuring reliable performance under such adverse conditions. Among them, single-image dehazing--which aims to restore clear scenes without relying on auxiliary depth information--remains especially difficult due to the ill-posed nature of the task. Early dehazing methods are primarily based on the atmospheric scattering model proposed by [PERSON] et al. [1]. As shown in Equation (1), this model restores a clear image by estimating the transmission map and global atmospheric light from a hazy input. \[I(x)=J(x)\,t(x)+A(1-t(x)), \tag{1}\]where \(I(x)\) denotes the observed hazy image, \(J(x)\) is the haze-free scene radiance, \(A\) is the global atmospheric light, and \(t(x)\) is the transmission map, which describes the portion of light that is not scattered and reaches the camera sensor. This process involves estimating the global atmospheric light and the transmission map to recover a clear image from a hazy observation. Based on this model, early studies [2, 3, 4, 5] attempted to perform image dehazing by leveraging prior knowledge derived from statistical observations to estimate the global atmospheric light and the transmission map. However, these approaches heavily rely on statistical assumptions that often do not hold in real-world scenarios. In particular, when the atmospheric light is spatially non-uniform or object colors closely resemble the atmospheric light, these priors may become unreliable, leading to suboptimal restoration performance. To address these limitations, recent approaches based on deep learning have been developed, focusing on learning a direct mapping from hazy to clear images via supervised learning. In particular, Vision Transformer (ViT)-based methods [6, 7, 8] utilize multi-head attention mechanisms and large effective receptive fields to produce high-quality dehazed outputs. Nonetheless, these methods typically involve substantial parameter counts and computational overhead, which limits their feasibility for real-time applications or deployment on devices with limited resources. To mitigate the computational burden of ViT-based approaches, recent studies have adopted Convolutional Neural Network (CNN) architectures to develop lightweight models that preserve large effective receptive fields [9, 10]. For instance, LKD-Net [11] incorporates the Large Kernel Attention (LKA) mechanism [12], achieving competitive performance with significantly fewer parameters--underscoring the computational efficiency of CNN-based frameworks. However, due to their reliance on fixed convolutional filters, CNN-based methods often struggle to capture diverse spatial relationships, particularly in complex visual scenes. To overcome these limitations, we propose the Multi-Head Large Kernel Block (MLKD), which combines the lightweight structure of CNNs with the rich spatial modeling power of ViT-based methods. The proposed MLKD is a lightweight CNN-based module that integrates Multi-Head Large Kernel Attention (MLKA), enabling effective spatial feature learning while preserving large effective receptive fields and maintaining a low parameter count. In the MLKA module, input channels are partitioned into multiple heads, with each independently processed using the Large Kernel Attention (LKA) mechanism. This configuration enhances spatial feature learning while sustaining large effective receptive fields with minimal computational overhead.