Multi-scale Transformer with Prompt Learning for Remote Sensing Image Dehazing

Jingru Wang, Xinguang Xiang

Abstract: Recently, Transformers have obtained decent performance in remote sensing (RS) image dehazing. However, most existing methods do not adequately consider the multi-scale properties of RS images and the non-homogeneous distribution of haze. To this end, we develop an effective RS image dehazing method based on multi-scale Transformer with prompt learning, named MPDformer. Specifically, our method contains two key designs: inter-scale prompt branch (ISPB) and crossscale dynamic feature fusion (CDFF). The ISPB dynamically provides guidance for the dehazing process to perceive spatially-varying haze distribution, thus facilitating the transfer of useful information across different scales. Simultaneously, the CDFF flexibly aggregates multi-scale features to facilitate representations learned at different scales to communicate with each other for better image restoration. Extensive experimental results on several RS dehazing benchmarks show that MPDformer achieves favorable performance against state-of-the-art approaches.