Aerial Image Dehazing with Attentive Deformable Transformers

Aerial imagery is widely utilized in visual data dependent applications such as military surveillance, earthquake assessment, etc. For these applications, minute texture in the aerial image are essential as any disturbance can cause inaccurate prediction. However, atmospheric haze severely reduces the visibility of the scene to be analysed, and hence takes a toll on accuracy of higher level applications. Existing methods either utilize additional prior while training, or produce sub-optimal outputs on different densities of haze degradation, due to absence of local and global dependencies in the extracted features. Therefore, it is essential to have a texture preserving algorithm for aerial image dehazing. In light of this, we propose a work that introduces a novel deformable multi-head attention with spatially attentive offset extraction based solution for aerial image dehazing. Here, the deformable multi-head attention is introduced to reconstruct fine level texture in the restored image. We also introduce spatially attentive offset extractor in the deformable convolution for focusing on relevant contextual information. Further, edge boosting skip connections are proposed for effectively passing edge features from shallow layers to deeper layers of the network. Thorough experimentation on synthetic as well as real-world data, along with extensive ablation study, demonstrate that the proposed method outperforms the prevailing works on aerial image dehazing. The code is provided at https://github.com/AshutoshKulkarni4998/AIDTransformer.