关键词: 图像去噪, 深度卷积神经网络, Inception模型, 批量规范化, 残差学习

Abstract: In order to remove the noise in images more effectively, a deep Convolutional Neural Network（CNN） combined with inception model is proposed for image denoising using the integrated image as input and output. Multiple spatial scale features are densely extracted through the inception structure to enhance the learning ability of the network. Rectified Linear Unit（ReLU） is used as an activate function to avoid the vanishing gradient problem. Batch Normalization （BN） and Residual Learning（RL） are utilized to speed up the training process as well as boost the overall denoising performance. The experimental results of three Gaussian noise levels based on the public dataset BSDS300 show that the proposed model has a 1.28 dB average Peak Signal to Noise Ratio（PSNR） increase and better visual results while reducing computational complexity and improving convergence rate.

Key words: image denoising, deep convolutional neural network, Inception model, batch normalization, residual learning