Abstract: To address the problems of generator training confusion, insufficient image detail restoration and incomplete haze removal in existing unsupervised image dehazing methods based on CycleGAN, an unsupervised image dehazing network based on High-frequency Information Enhancement (HIE-Net) is proposed. First, a Multi-Branch Dehazing Network (MBDN) is constructed. The network implements unified encoding of the image feature space through a shared encoding module, and adopts a multi-branch decoding module to achieve differentiated adaptation and precise decoding for features corresponding to different haze densities. Meanwhile, unsupervised constraints are established based on the Atmospheric Scattering Model (ASM) to regularize the training process of the generator. Secondly, a High-Frequency Multi-scale Enhancement Module (HMEM) is designed. A bidirectional guidance mechanism is built based on a large-kernel grouped attention gate. Through the bidirectional interaction between hazy region features and enhanced high-frequency information, the module synchronously achieves the multi-scale enhancement of both hazy region features and high-frequency information including image textures and edges. Finally, a Channel Feature Purification Module (CFPM) is introduced. A channel cross-attention mechanism is adopted to accurately screen haze-sensitive channels, suppress the interference caused by haze residuals in the feature fusion stage, and optimize the allocation of channel feature space. Meanwhile, a spatial cross-attention mechanism is leveraged to capture the haze density correlation and spatial dependency across different regions, thus achieving the fine-grained purification of deep features. Experimental results demonstrate that on the BeDDE dataset, HIE-Net achieves 21.20 dB, 0.779, and 0.198 in PSNR, SSIM, and LPIPS, respectively, which provides a novel insight for the field of image dehazing.

摘要： 针对现有基于CycleGAN的无监督图像去雾方法中存在的生成器训练混淆、图像细节恢复不足和雾效去除不彻底问题，提出了基于高频信息增强的无监督图像去雾网络(HIE-Net)。首先，构建多分支去雾网络(MBDN)，通过共享编码模块对图像特征空间进行统一编码，同时采用多分支解码模块针对不同雾浓度特征实现差异化适配与精准解码，并结合大气散射模型(ASM)构建无监督约束，规范生成器的训练过程；其次，设计高频多尺度增强模块(HMEM)，基于大核分组注意力门构建双向引导机制，通过雾区特征与增强后高频信息的双向交互，同步完成雾区特征与图像纹理、边缘等高频信息的多尺度增强；最后，引入通道特征提纯模块(CFPM)，通过通道交叉注意力机制精准筛选雾敏感通道，抑制特征融合阶段的雾残留干扰，优化通道特征空间分配，借助空间交叉注意力机制捕获不同区域的雾浓度关联与空间依赖关系，实现深度特征的精细化提纯。实验结果表明，HIE-Net在BeDDE数据集上，PSNR、SSIM和LPIPS分别达21.20 dB、0.779和0.198，为图像去雾领域提供了一种新思路。