基于Transformer和门控融合机制的图像去雾算法  

Image Dehazing Algorithm Based on Transformer and Gated Fusion Mechanism

作　　者：王燕[1] 陈燕燕 刘晶晶 胡津源 WANG Yan;CHEN Yan-Yan;LIU Jing-Jing;HU Jin-Yuan(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China)

机构地区：[1]兰州理工大学计算机与通信学院,兰州730050

出　　处：《计算机系统应用》2025年第2期1-10,共10页Computer Systems & Applications

基　　金：国家自然科学基金(62266030)。

摘　　要：针对现有的图像去雾算法仍然存在去雾不彻底、去雾后的图像边缘模糊、细节信息丢失等问题,本文提出了一种基于Transformer和门控融合机制的图像去雾算法.通过改进的通道自注意力机制提取图像的全局特征,提高模型处理图像的效率,设计多尺度门控融合块捕获不同尺度的特征,门控融合机制通过动态调整权重,提高模型对不同雾化程度的适应能力,同时更好地保留图像边缘及细节信息,并使用残差连接增强特征的重用性,提高模型泛化能力.经实验验证,所提出的去雾算法可以有效恢复真实有雾图像中的内容信息,在合成的有雾图像数据集SOTS上的峰值信噪比达到了34.841 dB,结构相似性达到了0.984,去雾后的图像内容信息完整且没有出现细节信息模糊和去雾不彻底等现象.Aiming at the existing image dehazing algorithms which still have problems such as incomplete dehazing,blurred edges of dehazed images,and detail information loss,this study presents an image dehazing algorithm based on Transformer and gated fusion mechanism.Global features of the image are extracted by the improved channel selfattention mechanism to improve the efficiency of the model in processing images.A multi-scale gated fusion block is designed to capture features of different scales.The gated fusion mechanism improves the adaptability of the model to different degrees of dehazing by dynamically adjusting weights while better preserving the image edges and detail information.Residual connections are used to enhance the reusability of features and improve the generalization ability of the model.Experimental verification shows that the proposed dehazing algorithm can effectively restore the content information in real hazy images.On the synthesized hazy image dataset SOTS,the peak signal-to-noise ratio reaches 34.841 dB,and the structural similarity reaches 0.984.The dehazed image has complete content information without blurred detail information and incomplete dehazing.

关 键 词：图像去雾 TRANSFORMER 自注意力机制 门控融合机制 多尺度特征融合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]