基于注意力机制的双路解码器图像去噪方法  

Dual Decoder Image Denoising Method Based on Attention Mechanism

在线阅读下载全文

作  者:高煜宝 文志诚 GAO Yubao;WEN Zhicheng(School of Computing,Hunan University of Technology,Zhuzhou 412007,Hunan,China)

机构地区:[1]湖南工业大学计算机学院,湖南株洲412007

出  处:《计算机工程》2024年第9期324-332,共9页Computer Engineering

基  金:国家自然科学基金(61871432);湖南省自然科学基金青年项目(2019JJ50123,2024JJ7154);湖南省教育厅项目(20C0625)。

摘  要:目前大多数图像去噪算法在去除图像噪声的同时,通常会丢失图像的细节信息,特别是当噪声强度较大时甚至会出现失真。随着当前神经网络结构普遍趋向于深层设计,导致图像的浅层特征难以与深层特征融合。针对这些问题,提出一种基于注意力机制的双路解码器图像去噪方法。首先,设计一种残差密集块(RDB)来对U-Net网络进行改进,实现网络深度的增加,有效提升模型的稳定性并缓解梯度消失问题;其次,设计一种双路解码器结构,通过在不同尺度的解码器中进行多尺度特征提取,加强深浅层特征的融合;最后,通过在解码器中引入注意力机制,有针对性地捕获图像的边缘信息,增强模型的去噪表现。实验结果表明,相较于现有常见的图像去噪方法,所提方法不仅能够有效去除图像噪声,还能更好地恢复图像纹理细节,同时具有较快的去噪速度,在主观和客观评价中均获得了更好的结果。Most current image denoising algorithms usually lose the details of an image while removing the image noise,especially when the noise intensity is large,and in some cases,distortion appears.Because the current neural network structure generally tends to have a deep-level design,fusing the shallow features of an image with deep features is difficult.To address these issues,an attention-based two-pass decoder approach for image denoising is proposed.First,a Residual Dense Block(RDB)is designed to increase the depth of the network by improving the U-Net network.The RDB increases the depth of the network,effectively improves the stability of the model,and alleviates the problem of gradient disappearance.Second,a dual decoder structure is designed through multi-scale feature extraction performed by decoders of different scales to strengthen the fusion of shallow and deep features.Third,by introducing an attention mechanism in the decoder,the edge information of the image is captured in a targeted manner to enhance the denoising performance of the model.Experiments prove that,compared with existing common image denoising methods,the proposed method not only effectively removes image noise but also better restores image texture details and has a faster denoising rate.Hence,the proposed method obtains better results in both subjective and objective evaluations.

关 键 词:深度学习 卷积神经网络 图像去噪 双路解码器 多尺度 

分 类 号:TP391[自动化与计算机技术—计算机应用技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象