Image Super Resolution Reconstruction Algorithm Based on Deep Learning
Huijing Dou, Wenqian Zhang, Xiao Liang · 2019
Super Resolution Convolutional Neural Network (SRCNN) solves the problems of poor robustness and complex calculation of traditional image super-resolution reconstruction algorithm, but its training data set and the number of layers of neural network is relatively small, and the edge and texture detail information are not handled well. For the above problems, the Maxout activation function is adopted in this paper to avoid the problems encountered by traditional activation functions such as gradient disappearance or overflow. Then the combination of Maxout and Dropout can train large data set and deepen neural network. Experimental results show that, compared with the classical algorithm, the algorithm proposed in this paper can train a large amount of data, improve the quality of reconstructed images and the generalization ability of the network model, and can enhance the robustness of the model.