AeroDehazeNet: Exploiting Selective Multi-Scale Transformers for Aerial Image Dehazing

Kartik Gonde, Prashant W. Patil, Santosh Kumar Vipparthi, Subrahmanyam Murala, Pramod Patil, Vinod Kimbahune

Abstract: Remote sensing is the task of analyzing and acquiring useful information from satellite images captured at a far distance from the earth’s surface. These images are vulnerable to degradation due to the presence of mist or haze. Existing methods either make use of prior information to estimate haze free images, or use CNN architectures based on generative adversarial networks (GANs) or Transformers. Though the state-of-the-art transformer-based architectures helped to dehaze the aerial images, they lacked the ability to capture multi-scale dependencies of the image. Identifying this shortcoming, we propose AeroDehazeNet based on a transformer that captures multi-scale dependencies along with global dependencies of the image. Our network comprises of three key components: (1) a multi-scale selective attention (MScA) network to attentively process the multi-scale information in an image, (2) residual attention network (RAN in feed forward network responsible for distilling non-degraded features passed from MScA, and (3) high frequency dominant skip connection (HFDS) block for passing diverse features (low frequency and high frequency) prominent with multi-scale edge features from encoder levels to adjacent decoder levels. The extensive quantitative and qualitative comparisons with existing methods on synthetic and realworld data plus exhaustive ablation study demonstrate the efficacy of our proposed network over transformer based state-of-the-art architectures with comparatively less number of parameters and FLOPs. Testing code is available at https://github.com/KartikGonde/AeroDehazeNet.