An Effective and Comprehensive Image Super Resolution Algorithm Combined With a Novel Convolutional Neural Network and Wavelet Transform

In order to further improve the reconstruction effect of the image super resolution algorithm, this paper proposes an image super resolution algorithm combining deep learning and wavelet transform (ISRDW). In terms of network design, it is not only simple in structure, but also more effective in capturing image details compared with other neural network structures. At the same time, cross-connection and residual learning methods are used to reduce the difficulty of the training model. In terms of loss function, this paper uses the loss generated in the original image space domain and the wavelet domain to strengthen the constraint of network training. Experimental results show that the algorithm proposed in this paper achieves better results under different data sets and different evaluation indexes.

Automated summary

This paper proposes an image super resolution algorithm combining deep learning and wavelet transform (ISRDW) that effectively captures image details, reduces training model difficulty with cross-connection and residual learning, and achieves better results through loss function constraint in network training.