文凯,季娟,薛晓,等.基于边缘特征融合网络的图像去噪方法[J]. 微电子学与计算机,2023,40(6):25-32. doi: 10.19304/J.ISSN1000-7180.2022.0514
引用本文: 文凯,季娟,薛晓,等.基于边缘特征融合网络的图像去噪方法[J]. 微电子学与计算机,2023,40(6):25-32. doi: 10.19304/J.ISSN1000-7180.2022.0514
WEN K,JI J,XUE X,et al. Image denoising method based on edge feature fusion network[J]. Microelectronics & Computer,2023,40(6):25-32. doi: 10.19304/J.ISSN1000-7180.2022.0514
Citation: WEN K,JI J,XUE X,et al. Image denoising method based on edge feature fusion network[J]. Microelectronics & Computer,2023,40(6):25-32. doi: 10.19304/J.ISSN1000-7180.2022.0514

基于边缘特征融合网络的图像去噪方法

Image denoising method based on edge feature fusion network

  • 摘要: 目前大多数图像去噪算法在去除噪声的同时,通常会丢失图像的边缘细节信息. 针对这一问题,提出了一种基于边缘特征融合的图像去噪方法. 首先,通过基于Canny算子的边缘提取网络提取图像的边缘信息,由于Canny算子不需要训练,这在很大程度上缩短了去噪时间;其次,通过基于残差密集连接的初去噪网络来保证训练的稳定性以及避免梯度消失,实现图像的初步去噪;最后,通过基于信道与空间注意力机制的融合网络将提取的边缘信息图像与初步去噪图像充分融合,自适应地给相对重要的边缘信息分配更大的权值,对图像的边缘细节进行增强,以得到具有更多边缘信息的清晰图像. 实验结果表明,在BSD68和Set12数据集上,与常见的DnCNN、BM3D等去噪方法相比,所提出去噪方法的平均PSNR比DnCNN分别高出0.13 dB、0.29 dB,比BM3D分别高出0.76 dB、0.82 dB,在视觉效果上看也保留了更多的图像细节,同时去噪速率也大幅度的提高.

     

    Abstract: Most of the current image denoising algorit hms usually cause the loss of edge detail information of the image while removing noise. Aiming at this problem, an image denoising method based on edge feature fusion is proposed. First,the edge information of the image is extracted by the edge extraction network based on Canny operator. Because the Canny operator does not need to be trained, the denoising time is shortened to a large extent. Secondly, the initial denoising network based on dense residual connections is used to ensure the stability of the training and avoid the disappearance of the gradient to achieve the initial denoising of the image. Finally, through the fusion network based on channel and spatial attention mechanism, the extracted edge information image is fully fused with the preliminary denoised image, and the relatively important edge information is adaptively allocated with more weight, and the edge details of the image are enhanced, so as to get a clear image with more edge information. Experimental results show that on BSD68 and Set12 datasets, compared with the common denoising methods such as DnCNN and BM3D, the average PSNR of the proposed denoising method is 0.13 dB and 0.29 dB higher than DnCNN, and 0.76 dB and 0.82 dB higher than BM3D, respectively. In terms of visual effects, more image details are retained, and the denoising rate is also greatly improved.

     

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