Abstract
Haze decreases contrast and limit sight in both outdoor and indoor images. Each pixel's deterioration is unique and is influenced by how far the scene point is from the camera. The transmission coefficients, which regulate the scene attenuation and degree of haze in each pixel, express this dependence. Previous techniques used a variety of patch-based priors and transformers to solve the single image dehazing problem. Although various researches had demonstrated the effectiveness of vision Transformers, our image dehazing method has been able to surpass the state-of-the-art image dehazing networks. As a result, we proposed a novel image dehazing network named Alternate Pooling Fused Transformer Network (APF_TRANS_NET) with Locally Grouped Self Attention. Compared to earlier deep learning-based methods, it performs far better. The proposed approach enhances the ability of vision transformer in Dehazing progress with an efficient transformer along with the dual weighted deep channel and spatial attention mechanism. To show the efficiency of our model, we trained it on five different datasets, including i-Haze dataset, O-Haze dataset, SOTS dataset, RESIDE-6K, and RS-Haze. The proposed our immense model outperforms the prior state-of-the-art techniques, with a significant improvement in its performance. .