# Haze-Aware Attention Network for Single-Image Dehazing
###### Abstract  
Single-image dehazing is a pivotal challenge in computer vision that seeks to remove haze from images and restore clean background details. Recognizing the limitations of traditional physical model-based methods and the inefficiencies of current attention-based solutions, we propose a new dehazing network combining an innovative Haze-Aware Attention Module (HAAM) with a Multiscale Frequency Enhancement Module (MFEM). The HAAM is inspired by the atmospheric scattering model, thus skillfully integrating physical principles into high-dimensional features for targeted dehazing. It picks up on latent features during the image restoration process, which gives a significant boost to the metrics, while the MFEM efficiently enhances high-frequency details, thus sidestepping wavelet or Fourier transform complexities. It employs multiscale fields to extract and emphasize key frequency components with minimal parameter overhead. Integrated into a simple U-Net framework, our Haze-Aware Attention Network (HAA-Net) for single-image dehazing significantly outperforms existing attention-based and transformer models in efficiency and effectiveness. Tested across various public datasets, the HAA-Net sets new performance benchmarks. Our work not only advances the field of image dehazing but also offers insights into the design of attention mechanisms for broader applications in computer vision. single-image dehazing; haze-aware attention; multiscale frequency enhancement module +
Footnote †: journal: Applied  
0000-0002-414-4098]Cited for 0000-0001-7794-2233]Cited for 0000-0001-7794-2233]Cited for 0000-0001-7794-22  
that of traditional physical model-based methods [1]. The PCFA module [9] taps feature pyramid and channel attention to extract and focus on crucial image features for effective dehazing. [PERSON] et al. [10] introduced an RMAM with an attention block to help networks concentrate on key features during learning. [PERSON] et al. [11] designed a network that teams up multilevel feature blending with mixed convolution attention to steadily and smartly boost dehazing results. However, the explanation behind these attention-based approaches remains unclear, as they do not have a solid link to the atmospheric scattering model [1, 12]. Additionally, some of these techniques [2, 13, 14] employ intricate designs or self-attention mechanisms, thus leading to suboptimal efficiency. In this study, we take a fresh look at the attention mechanism designs in dehazing networks and introduce a new approach inspired by physical priors [15, 16, 17], which is named the Haze-Aware Attention Module (HAAM). This module ingeniously applies the attention mechanism to mimic the parameters of the atmospheric scattering model, thus expressing these parameters through high-dimensional features. By employing these features constrained by the physical model, we conducted the dehazing process within the feature space, thus achieving outstanding results in both performance and efficiency. In addition to HAAM, we also developed a new Multiscale Frequency Enhancement Module (MFEM) designed to boost high-frequency details without relying on wavelet [18] or Fourier transforms [19]. MFEM uses a 4-scale receptive field to extract contextual features, thus fully adapting to receptive fields of different sizes. It also emphasizes important information in the frequency domain through lightweight kernel learning parameters in the channel dimension, thus effectively enhancing the dehazing effect. This approach sidesteps the extra computational work typically needed for these methods' reverse transformations, thus resulting in an efficient and stable enhancement of features. By combining our proposed HAAM and MFEM with the straightforward U-Net architecture, we have created the Haze-Aware Attention Network (HAA-Net) for single-image dehazing. Our method has been tested on both synthetic and real-world datasets.