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Remote sensing image reconstruction using an asymmetric multi-scale super-resolution network

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Abstract

High-resolution (HR) remote sensing images demonstrate detailed geographical information; however, some remote sensing satellites are incapable of offering HR remote sensing images because of the restriction of hardware resources. The single-image super-resolution (SISR) reconstruction technique is considered as an important method to improve the resolution of remote sensing images; however, most super-resolution (SR) image reconstruction methods are not able to sufficiently extract and utilize image features, and there is much redundancy of network parameters. To address this problem, an asymmetric multi-scale super-resolution network (AMSSRN) is proposed. In this network, a residual multi-scale block (RMSB) and a residual multi-scale dilation block (RMSDB) are designed to extract both shallow and deep features from images. This asymmetric structure enables the deep features to be fully extracted while reducing the redundancy of the modules used to extract shallow features. In this work, a feature-refinement fusion (FRF) module is also established, which can make full use of extracted features to improve network performance. Experimental results indicate that compared with enhanced deep super-resolution (EDSR) network, the proposed AMSSRN can efficiently reduce the redundancy of network parameters and enhance the feature extraction capability: the maximum increases in peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) index can reach 0.23 dB and 0.9782, respectively.

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Availability of data and materials

The data presented in this study are available on request from the corresponding author.

Abbreviations

HR:

High resolution

SISR:

Single-image super-resolution

SR:

Super-resolution

AMSSRN:

Asymmetric multi-scale super-resolution network

RMSB:

Residual multi-scale block

RMSDB:

Residual multi-scale dilation block

FRF:

Feature-refinement fusion

EDSR:

Enhanced deep super-resolution

PSNR:

Peak signal-to-noise ratio

SSIM:

Structural similarity

LR:

Low resolution

CNN:

Convolutional neural network

SRCNN:

Super-resolution convolutional neural network

FSRCNN:

Fast super-resolution convolutional neural network

ESPCN:

Efficient sub-pixel convolutional network

VDSR:

Very deep super-resolution

DRCN:

Deeply recursive convolutional network

DRRN:

Deep recursive residual network

SRDenseNet:

Super-resolution dense network

LapSRN:

Laplacian super-resolution network

MSRN:

Multi-scale residual network

IMDN:

Information multi-distillation network

RFDN:

Residual feature distillation network

SMSR:

Sparse masks super-resolution

MPSR:

Multi-perception super-resolution

URNet:

U-shaped residual network

Bicubic:

Bicubic interpolation

RL-NL:

Region-level non-local

MBRB:

Multi-branch residual block

PReLU:

Parametric rectified linear unit

MSE:

Mean square error

DIV2K:

Diverse 2K

AID:

Aerial image dataset

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Funding

This research was funded by the NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA, grant number 62001238.

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Authors and Affiliations

  1. School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Hai Huan

  2. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Nan Zou, Yaqin Xie & Chao Wang

  3. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Chao Wang

  4. Nanjing Nanji Agricultural Machinery Science and Technology Research Institute, Nanjing, 210019, China

    Yi Zhang

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  1. Hai Huan
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Contributions

H.H. took part in conceptualization; H.H. and N.Z. involved in methodology; N.Z. took part in software; N.Z. took part in validation; N.Z. involved in formal analysis; H.H. and N.Z. took part in investigation; H.H., C.W., Y.Z. involved in resources; N.Z. involved in writing—original draft preparation; H.H., N.Z., C.W., and Y.X. took part in writing—review and editing; H.H. and N.Z. involved in visualization; H.H., C.W., and Y.X. took part in supervision; H.H. involved in project administration. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Hai Huan.

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Huan, H., Zou, N., Zhang, Y. et al. Remote sensing image reconstruction using an asymmetric multi-scale super-resolution network. J Supercomput 78, 18524–18550 (2022). https://doi.org/10.1007/s11227-022-04617-x

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