基于多头注意力机制的单幅逆光图像卷积增强方法  

A Convolutional Enhancement Method for Single Backlit Image Based on Multihead Attention Mechanism

作　　者：宋雅丽[1] SONG Yali(Department of Information Engineering,Anhui Industry Polytechnic,Tongling 244000,China)

机构地区：[1]安徽工业职业技术学院信息工程系,安徽铜陵244000

出　　处：《常州工学院学报》2025年第1期31-36,77,共7页Journal of Changzhou Institute of Technology

基　　金：安徽省科研编制计划项目(2022AH053156)。

摘　　要：使用中值滤波与小波阈值相结合的去噪方法去除单幅逆光图像中包含的椒盐噪声以及高斯噪声,并通过在卷积神经网络中引入多头注意力机制,构建单幅逆光图像增强模型,之后将去噪后的单幅逆光图像输入所构建的单幅逆光图像增强模型中,通过多头注意力机制,有效辅助卷积神经网络关注单幅逆光图像欠曝光、过曝光区域,经梯度下降法实施有效模型训练后,输出增强后的逆光图像,完成单幅逆光图像增强工作。实验结果表明:该方法能够实现单幅逆光图像增强,增强效果较好,增强后逆光图像视觉效果明显强于未引入多头注意力机制前。A single backlight image convolution enhancement method based on multihead attention mechanism is studied to effectively enhance the quality of a single backlight image.A denoising method combining median filtering and wavelet thresholding is used to remove the salt and pepper noise and Gaussian noise contained in a single backlight image.By introducing a multihead attention mechanism into a con-volutional neural network,a single backlight image enhancement model is constructed.Then,the denoised single backlight image is input into the constructed single backlight image enhancement model,and the multihead attention mechanism is used,effectively assisting convolutional neural networks in focusing on underexposed and overexposed areas of a single backlight image.After implementing effective model training using gradient descent method,the enhanced backlight image is output to complete the single backlight image enhancement work.The experimental results show that this method can achieve single backlight image enhancement with good enhancement effect.The visual effect of the enhanced backlight image is significantly stronger than before the introduction of multihead attention mechanism.

关 键 词：多头注意力机制 逆光图像 图像增强 卷积神经网络 图像去噪 模型训练 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]