Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising
Affected by the hardware conditions and environment of imaging, images generally have serious noise. The presence of noise diminishes the image quality and compromises its effectiveness in real-world applications. Therefore, in real-world applications, reducing image noise and improving image qualit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/18/7713 |
| _version_ | 1797577087669764096 |
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| author | Shuo Zhang Chunyu Liu Yuxin Zhang Shuai Liu Xun Wang |
| author_facet | Shuo Zhang Chunyu Liu Yuxin Zhang Shuai Liu Xun Wang |
| author_sort | Shuo Zhang |
| collection | DOAJ |
| description | Affected by the hardware conditions and environment of imaging, images generally have serious noise. The presence of noise diminishes the image quality and compromises its effectiveness in real-world applications. Therefore, in real-world applications, reducing image noise and improving image quality are essential. Although current denoising algorithms can somewhat reduce noise, the process of noise removal may result in the loss of intricate details and adversely impact the overall image quality. Hence, to enhance the effectiveness of image denoising while preserving the intricate details of the image, this article presents a multi-scale feature learning convolutional neural network denoising algorithm (MSFLNet), which consists of three feature learning (FL) modules, a reconstruction generation module (RG), and a residual connection. The three FL modules help the algorithm learn the feature information of the image and improve the efficiency of denoising. The residual connection moves the shallow information that the model has learned to the deep layer, and RG helps the algorithm in image reconstruction and creation. Finally, our research indicates that our denoising method is effective. |
| first_indexed | 2024-03-10T22:03:03Z |
| format | Article |
| id | doaj.art-2169d867d84d4b328864293e71375b12 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:03:03Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2169d867d84d4b328864293e71375b122023-11-19T12:53:10ZengMDPI AGSensors1424-82202023-09-012318771310.3390/s23187713Multi-Scale Feature Learning Convolutional Neural Network for Image DenoisingShuo Zhang0Chunyu Liu1Yuxin Zhang2Shuai Liu3Xun Wang4Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaAffected by the hardware conditions and environment of imaging, images generally have serious noise. The presence of noise diminishes the image quality and compromises its effectiveness in real-world applications. Therefore, in real-world applications, reducing image noise and improving image quality are essential. Although current denoising algorithms can somewhat reduce noise, the process of noise removal may result in the loss of intricate details and adversely impact the overall image quality. Hence, to enhance the effectiveness of image denoising while preserving the intricate details of the image, this article presents a multi-scale feature learning convolutional neural network denoising algorithm (MSFLNet), which consists of three feature learning (FL) modules, a reconstruction generation module (RG), and a residual connection. The three FL modules help the algorithm learn the feature information of the image and improve the efficiency of denoising. The residual connection moves the shallow information that the model has learned to the deep layer, and RG helps the algorithm in image reconstruction and creation. Finally, our research indicates that our denoising method is effective.https://www.mdpi.com/1424-8220/23/18/7713multi-scale feature learningdenoising algorithmconvolutional neural network |
| spellingShingle | Shuo Zhang Chunyu Liu Yuxin Zhang Shuai Liu Xun Wang Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising Sensors multi-scale feature learning denoising algorithm convolutional neural network |
| title | Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising |
| title_full | Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising |
| title_fullStr | Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising |
| title_full_unstemmed | Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising |
| title_short | Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising |
| title_sort | multi scale feature learning convolutional neural network for image denoising |
| topic | multi-scale feature learning denoising algorithm convolutional neural network |
| url | https://www.mdpi.com/1424-8220/23/18/7713 |
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