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Gao, H., Zeng, J. and Zhao, Y. (2016) Super-Resolution Reconstruction Algorithm Based on Adaptive Convolution Kernel Size Selection. Applications of Digital Image Processing XXXIX, Article ID: 997120.

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• TITLE: Human Face Super-Resolution Based on Hybrid Algorithm

  AUTHORS: Jinfeng Xia, Zhizheng Yang, Fang Li, Yuanda Xu, Nan Ma, Chunxing Wang

  KEYWORDS: Face Hallucination Super Resolution, Convolutional Network Hybrid Algorithm

  JOURNAL NAME: Advances in Molecular Imaging, Vol.8 No.4, September 14, 2018

  ABSTRACT: Aiming at the problems of image super-resolution algorithm with many convolutional neural networks, such as large parameters, large computational complexity and blurred image texture, we propose a new algorithm model. The classical convolutional neural network is improved, the convolution kernel size is adjusted, and the parameters are reduced; the pooling layer is added to reduce the dimension. Reduced computational complexity, increased learning rate, and reduced training time. The iterative back-projection algorithm is combined with the convolutional neural network to create a new algorithm model. The experimental results show that compared with the traditional facial illusion method, the proposed method can obtain better performance.