关键词: 超分辨率重建, 卷积神经网络（CNN）, ISODATA聚类算法, Adam优化算法

Abstract: In order to solve the problem that single-frame image can be super-resolution reconstructed under different magnification conditions, this paper proposes a good method of super-resolution reconstruction based on Adam optimization for convolutional neural network (CNN). Firstly, ISODATA (iterative selforganizing data analysis) clustering algorithm is used to classify the trained image sets. Then, feature mapping is obtained by feature extraction and nonlinear mapping of input images in the convolution neural network optimized by Adam. Finally, the feature mapping is reconstructed by deconvolution to obtain a multi-scale enlarged reconstruction image in the convolution neural network optimized by Adam. It is verified by experiments that the reconstruction effect of this method under different magnification conditions is better than the traditional algorithm, and it has better performance in visual effect.

Key words: super-resolution reconstruction, convolution neural network (CNN), iterative selforganizing data analysis (ISODATA) clustering algorithm, Adam optimization algorithm