目前，在去雾领域鲜有将先验信息引入到以数据驱动的深度学习方法中的工作，且大多数基于深度学习的去雾网络通常对计算机内存和算力有较高要求。为解决上述问题，本文提出一个高频信息对齐的多尺度融合去雾网络（HFMS-Net）。网络框架采用循环模式：对于生成器，通过在轻量卷积神经网络的不同深度引入残差连接，以充分利用网络的中间层特征，实现多尺度特征融合；对于判别器，网络需对其输入进行纹理信息提取，逼近去雾图像和有雾图像之间的高频信息，使基于数据驱动的网络更具物理解释性。与PFDN相比，HFMS-Net在相同设置下以约1/5的内存占用取得了更优越的性能，PSNR和SSIM分别提升了0.71、0.016。通过大量的对比实验和消融实验证明本网络的去雾性能与现有算法相比有一定的提升，对纹理信息具有更高的保真度。

At present, there is little work in the field of dehazing that introduces prior information into data-driven deep learning methods, and most dehazing networks based on deep learning usually have high requirements on computer memory and computing power. To solve the above problems, this paper proposes a multi-scale fusion dehazing network for high-frequency information alignment (HFMS-Net). The network framework adoptes a cycyle pattern: for the generator, residual connections are introduced at different depths of the lightweight convolutional neural network to make full use of the intermediate layer features of the network to achieve multi-scale feature fusion; for the discriminator, the network needs to extract texture information on its input to approximate the high-frequency information between the dehazed image and the hazy image, making the data-driven network more physically interpretable. Compared with PFDN, HFMS-Net achieves superior performance with about 1/5 of the memory footprint under the same setting, and the PSNR and SSIM are improved by 0.71 and 0.016, respectively. Through a large number of comparative experiments and ablation experiments, it is proved that the dehazing performance of this network has a certain improvement compared with the existing algorithms, and higher fidelity to texture information.