Haar Wavelet-Based Attention Network for Image Dehazing
Sumit Laha, Hassan Foroosh · 2022 IEEE International Conference on Image Processing (ICIP) · 2022
Weather conditions such as haze, mist, and fog can degrade the image clarity in outdoor scenes. Although various image dehazing techniques exist in the literature, it is very challenging to remove non-homogeneous and/or densely concentrated homogeneous fog. We propose an end-to-end dehazing network1that utilizes attention-based learning from Haar wavelet coefficients. The model uses multi-scale wavelet transformation to break down feature maps into low- and high-frequency coefficients. The channel attention layer learns haze features while the spatial attention layer focuses on the feature location from these coefficients to further refine the output. Quantitative and visual analyses demonstrate that the proposed framework outperforms recent state-of-the-art methods in removing haze from dense and non-homogeneous hazy images while maintaining color accuracy.