Unsupervised remote sensing image super-resolution reconstruction method based on recurrent neural network

A cyclic neural network and super-resolution reconstruction technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as inability to use non-matching images, and achieve good results
CN109934771AActive Publication Date: 2019-06-25BEIHANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIHANG UNIV
Publication Date
2019-06-25

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Abstract

The invention discloses an unsupervised remote sensing image super-resolution reconstruction method based on a recurrent neural network. The whole network is composed of two circulation networks, thefirst circulation network takes a low-resolution training image xt as input, a high-resolution image y is generated through a first generation network, and then a low-resolution image G2 (y) is generated through a second generation network. The second circulation network takes the high-resolution training image yt as input, a low-resolution image x is generated through a second generation network,and the low-resolution image x generates a high-resolution image G1 (x) through the first generation network. And finally, reconstructing a low-resolution remote sensing image through the trained first generation network to obtain a high-resolution image. According to the reconstruction method, the non-paired high-resolution and low-resolution remote sensing image can be used for network training, and the problem that a traditional method cannot use a non-matched image pair for training is solved.
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Description

technical field

[0001] The present invention relates to the technical field of digital image processing, and more specifically relates to deep learning and image block feature extraction and reconstruction technology. Background technique

[0002] Remote sensing image super-resolution technology can effectively improve the resolution of remote sensing images, restore the details of remote sensing images, improve the visual effect of remote sensing images, perform target detection on super-resolution reconstructed remote sensing images, image region segmentation, etc., can effectively improve the processing effect . In recent years, with the continuous development of deep learning, super-resolution reconstruction algorithms based on deep neural networks have gradually become a research hotspot.

[0003] However, since most current deep neural network-based algorithms use supervised training methods, that is, training matched low-resolution-high-resolution image pairs, but in...

Claims

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