A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion
Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge fea...
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2025-01-01
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| author | Jing Mao Lianming Sun Jie Chen Shunyuan Yu |
| author_facet | Jing Mao Lianming Sun Jie Chen Shunyuan Yu |
| author_sort | Jing Mao |
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| description | Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. To solve these problems, this paper proposes a two-branch convolutional image denoising network based on nonparametric attention and multiscale feature fusion, aiming to improve the denoising performance while better recovering the image edge and texture information. Firstly, ordinary convolutional layers were used to extract shallow features of noise in the image. Then, a combination of two-branch networks with different and complementary structures was used to extract deep features from the noise information in the image to solve the problem of insufficient feature extraction by the single-branch network model. The upper branch network used densely connected blocks to extract local features of the noise in the image. The lower branch network used multiple dilation convolution residual blocks with different dilation rates to increase the receptive field and extend more contextual information to obtain the global features of the noise in the image. It not only solved the problem of insufficient edge feature extraction but also solved the problem of the saturation of deep CNN performance. In this paper, a nonparametric attention mechanism is introduced in the two-branch feature extraction module, which enabled the network to pay attention to and learn the key information in the feature map, and improved the learning performance of the network. The enhanced features were then processed through the multiscale feature fusion module to obtain multiscale image feature information at different depths to obtain more robust fused features. Finally, the shallow features and deep features were summed using a long jump join and were processed through an ordinary convolutional layer and output to obtain a residual image. In this paper, Set12, BSD68, Set5, CBSD68, and SIDD are used as a test dataset to which different intensities of Gaussian white noise were added for testing and compared with several mainstream denoising methods currently available. The experimental results showed that this paper’s algorithm had better objective indexes on all test sets and outperformed the comparison algorithms. The method in this paper not only achieved a good denoising effect but also effectively retained the edge and texture information of the original image. The proposed method provided a new idea for the study of deep neural networks in the field of image denoising. |
| format | Article |
| id | doaj-art-3a812d5229b240b881ccd24688ac2e97 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3a812d5229b240b881ccd24688ac2e972025-01-24T13:48:29ZengMDPI AGSensors1424-82202025-01-0125231710.3390/s25020317A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature FusionJing Mao0Lianming Sun1Jie Chen2Shunyuan Yu3Graduate School of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, JapanDepartment of Information Systems Engineering, The University of Kitakyushu, Kitakyushu 808-0135, JapanSchool of Electronic and Information Engineering, Ankang University, Ankang 725000, ChinaSchool of Electronic and Information Engineering, Ankang University, Ankang 725000, ChinaConvolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. To solve these problems, this paper proposes a two-branch convolutional image denoising network based on nonparametric attention and multiscale feature fusion, aiming to improve the denoising performance while better recovering the image edge and texture information. Firstly, ordinary convolutional layers were used to extract shallow features of noise in the image. Then, a combination of two-branch networks with different and complementary structures was used to extract deep features from the noise information in the image to solve the problem of insufficient feature extraction by the single-branch network model. The upper branch network used densely connected blocks to extract local features of the noise in the image. The lower branch network used multiple dilation convolution residual blocks with different dilation rates to increase the receptive field and extend more contextual information to obtain the global features of the noise in the image. It not only solved the problem of insufficient edge feature extraction but also solved the problem of the saturation of deep CNN performance. In this paper, a nonparametric attention mechanism is introduced in the two-branch feature extraction module, which enabled the network to pay attention to and learn the key information in the feature map, and improved the learning performance of the network. The enhanced features were then processed through the multiscale feature fusion module to obtain multiscale image feature information at different depths to obtain more robust fused features. Finally, the shallow features and deep features were summed using a long jump join and were processed through an ordinary convolutional layer and output to obtain a residual image. In this paper, Set12, BSD68, Set5, CBSD68, and SIDD are used as a test dataset to which different intensities of Gaussian white noise were added for testing and compared with several mainstream denoising methods currently available. The experimental results showed that this paper’s algorithm had better objective indexes on all test sets and outperformed the comparison algorithms. The method in this paper not only achieved a good denoising effect but also effectively retained the edge and texture information of the original image. The proposed method provided a new idea for the study of deep neural networks in the field of image denoising.https://www.mdpi.com/1424-8220/25/2/317image denoisingdeep learningnonparametric attentiondilation convolutionresidual learning |
| spellingShingle | Jing Mao Lianming Sun Jie Chen Shunyuan Yu A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion Sensors image denoising deep learning nonparametric attention dilation convolution residual learning |
| title | A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion |
| title_full | A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion |
| title_fullStr | A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion |
| title_full_unstemmed | A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion |
| title_short | A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion |
| title_sort | parallel image denoising network based on nonparametric attention and multiscale feature fusion |
| topic | image denoising deep learning nonparametric attention dilation convolution residual learning |
| url | https://www.mdpi.com/1424-8220/25/2/317 |
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