基于双支特征联合映射的端到端图像去雾算法
End-to-end Image Dehazing Algorithm Based on Joint Mapping of Two-Branch Features
杨燕 1陈阳1
作者信息
- 1. 兰州交通大学 电子与信息工程学院,甘肃 兰州 730070
- 折叠
摘要
针对卷积神经网络去雾算法中模型复杂度高、特征提取性能差等问题,本文提出了一种基于双支特征联合映射的端到端图像去雾算法.首先对大气散射模型进行变形转换,分离出模型中的双支特征;然后根据双支特点设计了两个特征提取网络MPFEM和SPFEM,分别使用两种注意力机制对其输出特征进行加权;最后将提取到的双支特征输入复原模块恢复清晰图像,并对其进行色彩增强得到最终复原效果.在模型训练过程中为避免使用单一损失函数导致纹理细节丢失等问题,采用多尺度结构相似度和平均绝对误差加权作为损失函数.实验表明,本文所提算法网络结构简单,去雾效果明显,复原图像色彩亮度保真,边缘保持性强.
Abstract
To address the issues of high model complexity and poor feature extraction performance in Convolutional neural network-based dehazing algorithms,this paper proposes an end-to-end image dehazing algorithm based on joint mapping of two-branch features.Firstly,the atmospheric scattering model is transformed to separate the mixed-parameter feature and the single-parameter feature model.Then two feature extraction networks,MPFEM and SPFEM are designed according to the two-branch features and the outputs are weighted by two attention mechanisms.Finally,the extracted two-branch features are sent to the restoration module to restore the clear image and perform color-enhancing to obtain the final restored effect.To avoid the loss of texture details caused by using a single loss function in the model training process,multi-scale structure similarity and mean absolute error weighting are used as the loss function.Experimental results show that the proposed algorithm has a simple network structure,obvious dehazing effect,accurate color brightness restoration,and strong edge preservation.
关键词
图像去雾/卷积神经网络/双支特征/注意力机制Key words
image dehazing/convolutional neural network/two-branch features/attention mechanism引用本文复制引用
基金项目
国家自然科学基金资助项目(61561030)
甘肃省高等学校产业支撑计划项目(2021CYZC-04)
兰州交通大学教改项目(JG201928)
出版年
2024
国家科技期刊平台
NSTL
万方数据