关键词: 图像去噪, 深度学习, 纹理细节, 降噪自编码器, 稀疏自编码器

Abstract: In order to improve the image denoising performance of Stacked Sparse Denoising Auto-encoder（SSDA）and solve the problems of high computational complexity, difficult parameters adjustment and slow training convergence speed, the image denoising algorithm based on Stacked Marginalized Sparse Denoising Auto-encoder（SMSDA） is proposed. Marginalized Sparse Denoising Auto-encoder（MSDA） is formed by marginalizing the loss function of SDA network because of the fast convergence speed of the Marginalized Denoising Auto-encoder（MDA）, which has the characteristics of SAE and MDA. Then, multiple MSDA is stacked to form deep neural network SMSDA. The unsupervised greedy layer-wise training algorithm is used to train each layer of network for avoiding the local optimization of the model parameters. The BP（Back Propagation） algorithm is used to fine tune the whole network and can obtain the optimal weight. Last, SMSDA is used to denoise a given image. Simulation results show that the proposed algorithm has higher Peak Signal to Noise Ratio（PSNR） while reducing the computational complexity and improving the convergence rate, retains more details of the original image and has better denoising performance than SSDA.

Key words: image denoising, deep learning, texture detail, Denoising Auto-Encoder（DAE）, Sparse Denoising Auto-encoder（SDA）