Abstract
In order to remove the Gaussian noise in the image more effectively, a convolutional auto-encoder image denoising model combined with the perception module is proposed. The model takes the whole image as input and output, uses the concept module to denoise the input noise image, uses the improved concept deconvolution module to restore the denoised image, and improves the denoising ability of the model. At the same time, the batch normalization (BN) layer and the random deactivation layer (Dropout) are introduced into the model to effectively solve the model over fitting problem, and the ReLu function is introduced to avoid the model gradient disappearing and accelerate the network training. The experimental results show that the improved convolution neural network model has higher peak signal-to-noise ratio and structure similarity, better denoising ability, better visual effect and better robustness than the deep convolution neural network model.
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, D., Gan, W., Yan, C., Huang, K., Wu, H. (2020). Inception Model of Convolutional Auto-encoder for Image Denoising. In: Liu, J., Gao, H., Yin, Y., Bi, Z. (eds) Mobile Computing, Applications, and Services. MobiCASE 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-64214-3_12
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DOI: https://doi.org/10.1007/978-3-030-64214-3_12
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