GLUENet: An Efficient Network for Remote Sensing Image Dehazing with Gated Linear Units and Efficient Channel Attention

Dehazing individual remote sensing (RS) images is an effective approach to enhance the quality of hazy remote sensing imagery. However, current dehazing methods exhibit substantial systemic and computational complexity. Such complexity not only hampers the straightforward analysis and comparison of these methods but also undermines their practical effectiveness on actual data, attributed to the overtraining and overfitting of model parameters. To mitigate these issues, we introduce a novel dehazing network for non-uniformly hazy RS images: GLUENet, designed for both lightweightness and computational efficiency. Our approach commences with the implementation of the classical U-Net, integrated with both local and global residuals, establishing a robust base for the extraction of multi-scale information. Subsequently, we construct basic convolutional blocks using gated linear units and efficient channel attention, incorporating depth-separable convolutional layers to efficiently aggregate spatial information and transform features. Additionally, we introduce a fusion block based on efficient channel attention, facilitating the fusion of information from different stages in both encoding and decoding to enhance the recovery of texture details. GLUENet’s efficacy was evaluated using both synthetic and real remote sensing dehazing datasets, providing a comprehensive assessment of its performance. The experimental results demonstrate that GLUENet’s performance is on par with state-of-the-art (SOTA) methods and surpasses the SOTA methods on our proposed real remote sensing dataset. Our method on the real remote sensing dehazing dataset has an improvement of 0.31 dB for the PSNR metric and 0.13 for the SSIM metric, and the number of parameters and computations of the model are much lower than the optimal method.