IMPROVEMENTS OF THE IMAGE SUPER-RESOLUTION ALGORITHM BASED ON CONVOLUTIONAL NEURAL NETWORKS
Journal: Applied Computer Letters (ACL)
Author: Claude M. Siefert*
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Face hallucination is a branch of image super-resolution, which develops domain specific prior knowledge with strong cohesion to face domain. As the development of machine learning, there are numerous learning-based methods which have been proposed to solve the face hallucination problem. Learning based algorithms have been seen to achieve higher magnification factor with better visual quality than the other super resolution techniques such as bi-cubic interpolation and reconstruction based techniques. In the paper, we apply the deep learning theory to illusory face hallucination reconstruction. The model of deep convolution neural network is improved, the convolution neural network is added to the pool layer, the convolution kernel size is adjusted, the parameters are reduced, and the operation speed is increased. Finally, the iterative back projection method is used to reconstruct the face image after post-processing.