Image denoising method, system and device based on transfer learning and medium
A transfer learning and image technology, applied in the field of image denoising based on transfer learning, can solve the problems of batch size dependence and noise influence, and achieve the effect of improving robustness and performance
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[0037] Embodiment 1, this embodiment provides an image denoising method based on transfer learning;
[0038] like figure 1 As shown, the image denoising method based on transfer learning includes:
[0039] Obtain the image to be denoised;
[0040] Input the image to be denoised into a pre-trained denoising neural network based on migration learning for processing, and the denoising neural network based on migration learning includes: a main noise reduction network and a noise distribution information extraction network;
[0041] The noise distribution information extraction network is used to extract random noise distribution features; after preprocessing, the random noise distribution features are used as the dynamic normalization parameters of each residual module of the main noise reduction network, and the random noise distribution features are transferred to the main denoising network. In the data features of the network, the convergence speed of the main network is acc...
Example Embodiment
[0160] Embodiment 2, this embodiment also provides an image denoising system based on transfer learning;
[0161] Image denoising system based on transfer learning, including:
[0162] The acquisition module is used to acquire the image to be denoised;
[0163] The noise reduction processing module is used to input the image to be denoised into the pre-trained denoising neural network based on migration learning for processing. The denoising neural network based on migration learning includes: a main noise reduction network and a noise reduction network. Distributed information extraction network;
[0164] The noise distribution information extraction network is used to extract random noise distribution features; after preprocessing, the random noise distribution features are used as the dynamic normalization parameters of each residual module of the main noise reduction network, and the random noise distribution features are transferred to the main denoising network. In the...
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