一种多映射卷积神经网络的超分辨率重建算法

doi:10.3969/j.issn.1001-2400.2018.04.027

Abstract

Abstract:

The traditional convolutional neural network for super-resolution will obtain abundant details and edge information with difficulty. By the analysis of the detailed characteristics in three modules of conventional methods, we propose a new multi-mapping convolutional neural network model. By the multi-mapping module, rich and varied characteristics from each layer can be captured. Combining with the error back propagation algorithm, a novel loss function with total variation regularization is used to train and seek optimal parameters, which reconstruct accurate and effective high-resolution images from the network. Extensive quantitative and qualitative evaluations have shown that the proposed algorithm improves effectively the resolution of the image.

Key words: image processing, super-resolution, multi-mapping convolutional network, constrained variation method

WANG Shiping;BI Duyan;LIU Kun;HE Linyuan. Multi-mapping convolution neural network for the image super-resolution algorithm[J].Journal of Xidian University, 2018, 45(4): 155-160.

References

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Multi-mapping convolution neural network for the image super-resolution algorithm

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