An image denoising method based on depth learning

In this paper, image noise reduction research is carried out based on in-depth learning. In specific life, due to the lack of perfection of equipment and system, the image will often be polluted by more noise, resulting in unclear image details and reduced image clarity. Better image display ability can be obtained when BP neural network is used to denoise the image. Through the research on the activation function and optimization network function based on weighted neural network (CNN), combined with multi feature extraction technology and other in-depth learning models, we can learn and extract the important features of the input image. At the same time, we propose CNN back propagation optimization algorithm. At the same time, the training speed of the model is improved and the convergence speed of the algorithm is accelerated. Based on the deep residual learning of convolution network, the algorithm is used to remove the noise in the model. This is a better image denoising network model. Compared with other excellent denoising algorithms, the analysis and comparison show that the optimized denoising algorithm can not reduce the clarity of the image. At the same time, the image noise pollution is greatly improved and the image details are clearer.