湖南大学学报（自然科学版）2025，Vol.52Issue(4)：16-26,11.DOI:10.16339/j.cnki.hdxbzkb.2025262
知识迁移引导的空频双域联合去雾网络
Knowledge Transfer Guided Space Frequency Dual Domain Joint Defogging Network
摘要
Abstract
The current CNN-based methods exhibit satisfactory performance in fog removal,but their network robustness is compromised due to the intricate haze distribution and challenging dataset collection.Consequently,there is a significant loss of texture details during the fog removal process and severe overfitting issues on small-scale data sets.To address these challenges,we propose a two-branch structure incorporating space-frequency joint techniques.The upper branch focuses on capturing finer texture details by utilizing three-level wavelet transform to extract features in the frequency domain.Meanwhile,the lower branch enhances network generalization by employing domain migration method to incorporate additional prior information from airspace and leveraging Res2Net as its core component.Finally,the proposed model is trained on the NH-HAZE dataset and evaluated for generalization ability using the I-HAZE and NTIRE 2023 datasets.Furthermore,to ensure fairness in comparison experiments,all competing algorithms are also trained using the NH-HAZE dataset.Experimental results demonstrate that our proposed network significantly improves both detail texture recovery capability and generalization performance.关键词
图像去雾/域迁移/小波变换/注意力机制/深度学习Key words
image dehazing/domain migration/wavelet transform/attention mechanism/deep learning分类
计算机与自动化引用本文复制引用
杨燕,梁皓博,林雷..知识迁移引导的空频双域联合去雾网络[J].湖南大学学报（自然科学版）,2025,52(4):16-26,11.基金项目
甘肃省高等学校产业支撑计划项目(2021CYZC-04),Gansu University Industry Support Program(2021CYZC-04) （2021CYZC-04）
兰州交通大学研究生教改项目(JG201928),Graduate Education Reform Project of Lanzhou Jiaotong University(JG201928) （JG201928）
