HazeTrendNet: Single‐Image Dehazing via Haze‐Concentration‐Trend Guidance
Chen Wang, Yuanyuan Fan · Electronics Letters · 2025
ABSTRACT Single‐image dehazing is vital for restoring clear visuals from haze‐affected images in applications like surveillance and autonomous driving. Most existing models struggle with local haze variations and detail preservation in dense haze. This study introduces HazeTrendNet, a lightweight dehazing framework, incorporating haze‐concentration‐trend guidance via transmittance estimation, dynamic convolution kernel selection and haze‐aware attention. Experiments show state‐of‐the‐art performance: PSNR/SSIM of 42.29 dB/0.997 on RESIDE‐Indoor, 17.68 dB/0.655 on Dense‐Haze and 22.14 dB/0.812 on NH‐HAZE, outperforming EENet, SANet and FocalNet. With 6.17 M parameters and 56.46 GFLOPs, it suits edge deployment.