Spatial-Frequency Adaptive Remote Sensing Image Dehazing With Mixture of Experts

The feature modulation mechanism has been demonstrated to be particularly well-suited for efficient network design and is rarely explored in remote sensing dehazing tasks. Moreover, we observe distinct patterns in haze distribution across the low-frequency (LF) and high-frequency (HF) components of haze images from various datasets. However, existing research rarely investigated the potential solution in the frequency domain. In response, we propose a novel spatial-frequency adaptive network (SFAN), which is mainly built by the proposed mixture of modulation experts (MoME) and decoupled frequency learning block (DFLB). Different from the fixed feature modulation design used in other tasks, the MoME adopts the mixture-of-expert mechanism to dynamically learn diverse contextual features of various granularities and scales in a sample-adaptive manner and then utilize them to perform elementwise local feature modulation. This pure convolution architecture enables our network to have superior performance and efficiency tradeoffs. Furthermore, the DFLB is devised to facilitate the LF global haze removal and reconstruction of HF local texture information. At the micro level, we first utilize a mask extractor (ME) to generate the frequency mask from the input hazy image, then employ a dual-branch decoupled learning unit to boost frequency learning, and finally develop a mixture of fusion experts (MoFE) to achieve HF and LF feature interaction. Extensive experiments on publicly available dehazing datasets demonstrate that our network performs superior performance while incurring lower computational costs. Compared to the state-of-the-art approach (DEA-Net), SFAN achieves, an average, 0.83-dB PSNR improvement on five remote sensing datasets but consumes only 51% of the FLOPs. The code will be available at https://github.com/it-hao/SFAN.