光学精密工程2025，Vol.33Issue(6)：916-927,12.DOI:10.37188/OPE.20253306.0916
频域特征蒸馏的双尺度融合图像去雾网络
Dual scale fusion image dehazing algorithm based on frequency-domain feature distillation
摘要
Abstract
Aimed at the issue that the edge details of the dehazing images were insufficiently clear,and the majority of the existing U-Net dehazing networks did not adequately exploit the information in the frequen-cy domain and neglected the information exchange among different channels,resulting in a blurry struc-ture,a dual-scale fusion network with frequency-domain feature distillation was proposed for the effective dehazing of single images.In the Coarse-scale feature extraction subnet,a large-scale convolution kernel was utilized to extract image texture information,and a residual attention mechanism was employed to en-hance the features related to haze.In the Fine-scale high-frequency fusion subnet,a high-frequency feature distillation module was devised to refine the extracted structure and edge information and gradually restore clear images.Meanwhile,the cross-fusion strategy was adopted to fuse the features of different channels.The experimental results indicate that compared with the MSTN algorithm(Efficient and Accurate Multi-Scale Topological Network),the peak signal-to-noise ratio and structural similarity on the outdoor image dataset have been enhanced by 9.98%and 4.77%respectively.The experimental results on diverse datas-ets demonstrate that the proposed approach exhibits superior performance.This method can effectively en-hance the dehazing effect,retain more structural information,and possess better color detail recovery capa-bility.关键词
高频信息/特征蒸馏/图像去雾/交叉融合/残差注意力Key words
high-frequency information/characteristic distillation/image dehazing/cross fusion/residu-al attention分类
计算机与自动化引用本文复制引用
陈清江,杨双..频域特征蒸馏的双尺度融合图像去雾网络[J].光学精密工程,2025,33(6):916-927,12.基金项目
国家自然科学基金(No.12202332) （No.12202332）
陕西省自然科学基础研究计划项目(No.2021JQ-495) （No.2021JQ-495）
