Research Article
Inception Model of Convolutional Auto-encoder for Image Denoising
@INPROCEEDINGS{10.1007/978-3-030-64214-3_12, author={Diangang Wang and Wei Gan and Chenyang Yan and Kun Huang and Hongyi Wu}, title={Inception Model of Convolutional Auto-encoder for Image Denoising}, proceedings={Mobile Computing, Applications, and Services. 11th EAI International Conference, MobiCASE 2020, Shanghai, China, September 12, 2020, Proceedings}, proceedings_a={MOBICASE}, year={2020}, month={12}, keywords={Convolutional auto-encoder Inception module Image denoising Peak signal to noise ratio Structural similarity}, doi={10.1007/978-3-030-64214-3_12} }- Diangang Wang
Wei Gan
Chenyang Yan
Kun Huang
Hongyi Wu
Year: 2020
Inception Model of Convolutional Auto-encoder for Image Denoising
MOBICASE
Springer
DOI: 10.1007/978-3-030-64214-3_12
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.
