Color image hybrid noise filtering algorithm based on deep convolution neural network
To solve the problems of the classical color image hybrid noise filtering method, a deep convolutional neural network improved by evolutionary strategy and jump connection is proposed and applied to the filtering noise reduction of color images. First, the color information of the image is described...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000498 |
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| author | Yongfei Yu Yuanjian Yan |
| author_facet | Yongfei Yu Yuanjian Yan |
| author_sort | Yongfei Yu |
| collection | DOAJ |
| description | To solve the problems of the classical color image hybrid noise filtering method, a deep convolutional neural network improved by evolutionary strategy and jump connection is proposed and applied to the filtering noise reduction of color images. First, the color information of the image is described quantitatively by digital means. The common method is to build color space model. According to the characteristics of color and the needs of human vision, mathematical algorithms are used to convert images into machine recognizable data. The distance between pixels is measured according to the difference of pixels in the color image determined above. Then, the probability density function and noise probability density function of Gaussian noise are calculated to determine the hybrid noise feature points of color image. The filtering algorithm structure designed this time is as follows: A color image hybrid noise filter is used to map the noise points in the mapped image to the feature space, and linear regression is performed on the noise point data. Relaxation variables are introduced in the network to improve the denoising ability. The experimental results show that the Peak Signal to Noise Ratio and structural similarity index values of the filtering algorithm designed in this study are higher than the two methods in the literature. The color image hybrid noise filtering model designed in this study has good filtering performance, good image cleanliness, and high filtering efficiency. |
| format | Article |
| id | doaj-art-eaf67dc6bbfc4abfa232012de8d662ea |
| institution | OA Journals |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-eaf67dc6bbfc4abfa232012de8d662ea2025-08-20T02:32:15ZengElsevierSystems and Soft Computing2772-94192024-12-01620012010.1016/j.sasc.2024.200120Color image hybrid noise filtering algorithm based on deep convolution neural networkYongfei Yu0Yuanjian Yan1School of Artificial Intelligence, Hefei College of Finance & Economics, Hefei, 230601, PR ChinaSchool of Art, Nanjing University of Information Science & Technology, Nanjing, 210044, PR China; Corresponding author.To solve the problems of the classical color image hybrid noise filtering method, a deep convolutional neural network improved by evolutionary strategy and jump connection is proposed and applied to the filtering noise reduction of color images. First, the color information of the image is described quantitatively by digital means. The common method is to build color space model. According to the characteristics of color and the needs of human vision, mathematical algorithms are used to convert images into machine recognizable data. The distance between pixels is measured according to the difference of pixels in the color image determined above. Then, the probability density function and noise probability density function of Gaussian noise are calculated to determine the hybrid noise feature points of color image. The filtering algorithm structure designed this time is as follows: A color image hybrid noise filter is used to map the noise points in the mapped image to the feature space, and linear regression is performed on the noise point data. Relaxation variables are introduced in the network to improve the denoising ability. The experimental results show that the Peak Signal to Noise Ratio and structural similarity index values of the filtering algorithm designed in this study are higher than the two methods in the literature. The color image hybrid noise filtering model designed in this study has good filtering performance, good image cleanliness, and high filtering efficiency.http://www.sciencedirect.com/science/article/pii/S2772941924000498Deep convolution neural networkColor imageHybrid noiseDifference value |
| spellingShingle | Yongfei Yu Yuanjian Yan Color image hybrid noise filtering algorithm based on deep convolution neural network Systems and Soft Computing Deep convolution neural network Color image Hybrid noise Difference value |
| title | Color image hybrid noise filtering algorithm based on deep convolution neural network |
| title_full | Color image hybrid noise filtering algorithm based on deep convolution neural network |
| title_fullStr | Color image hybrid noise filtering algorithm based on deep convolution neural network |
| title_full_unstemmed | Color image hybrid noise filtering algorithm based on deep convolution neural network |
| title_short | Color image hybrid noise filtering algorithm based on deep convolution neural network |
| title_sort | color image hybrid noise filtering algorithm based on deep convolution neural network |
| topic | Deep convolution neural network Color image Hybrid noise Difference value |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000498 |
| work_keys_str_mv | AT yongfeiyu colorimagehybridnoisefilteringalgorithmbasedondeepconvolutionneuralnetwork AT yuanjianyan colorimagehybridnoisefilteringalgorithmbasedondeepconvolutionneuralnetwork |