Multi-Dimensional Weight Regulation Network for Remote Sensing Image Dehazing

This paper introduces a lightweight remote sensing image dehazing network called multi-dimensional weight regulation network (MDWR-Net), which addresses the high computational cost of existing methods. Previous works, often based on the encoder-decoder structure and utilizing multiple upsampling and downsampling layers, are computationally expensive. To improve efficiency, the paper proposes two modules: the efficient spatial resolution recovery module (ESRR) for upsampling and the efficient depth information augmentation module (EDIA) for downsampling. These modules not only reduce model complexity but also enhance performance. Additionally, the partial feature weight learning module (PFWL) is introduced to reduce the computational burden by applying weight learning across partial dimensions, rather than using full-channel convolution. To overcome the limitations of convolutional neural networks (CNN)-based networks, the haze distribution index transformer (HDIT) is integrated into the decoder. We also propose the physical-based non-adjacent feature fusion module (PNFF), which leverages the atmospheric scattering model to improve generalization of our MDWR-Net. The MDWR-Net achieves superior dehazing performance with a computational cost of just 2.98×10⁹ multiply-accumulate operations (MACs), which is less than one-tenth of previous methods. Experimental results validate its effectiveness in balancing performance and computational efficiency.