Remote sensing image super-resolution using multi-scale convolutional sparse coding network

With the development of convolutional neural networks, impressive success has been achieved in remote sensing image super-resolution. However, the performance of super-resolution reconstruction is unsatisfactory due to the lack of details in remote sensing images when compared to natural images. The...

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Bibliographic Details
Published in:PloS one 2022-10, Vol.17 (10), p.e0276648-e0276648
Main Authors: Cheng, Ruihong, Wang, Huajun, Luo, Ping
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Biology and Life Sciences
Coding
Computer and Information Sciences
Convolution
Convolutional codes
Deep learning
Dictionaries
Earth Sciences
Engineering and Technology
Feature extraction
Image reconstruction
Image resolution
Methods
Neural networks
Remote sensing
Research and Analysis Methods
Signal to noise ratio
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Summary:With the development of convolutional neural networks, impressive success has been achieved in remote sensing image super-resolution. However, the performance of super-resolution reconstruction is unsatisfactory due to the lack of details in remote sensing images when compared to natural images. Therefore, this paper presents a novel multiscale convolutional sparse coding network (MCSCN) to carry out the remote sensing images SR reconstruction with rich details. The MCSCN, which consists of a multiscale convolutional sparse coding module (MCSCM) with dictionary convolution units, can improve the extraction of high frequency features. We can obtain more plentiful feature information by combining multiple sizes of sparse features. Finally, a layer based on sub-pixel convolution that combines global and local features takes as the reconstruction block. The experimental results show that the MCSCN gains an advantage over several existing state-of-the-art methods in terms of peak signal-to-noise ratio and structural similarity.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0276648