  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.
