Deep ResNet Based Remote Sensing Image Super-Resolution Reconstruction in Discrete Wavelet Domain

We present a single-image super-resolution (SR) method for Remote Sensing Image based on deep learning within Discrete Wavelet Domain in this paper. Our method is inspired Residual Learning. Firstly, an input image is decomposed by single level 2D Discrete wavelet transform to get four sub-bands. Th...

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Bibliographic Details
Published in:Pattern recognition and image analysis 2020-07, Vol.30 (3), p.541-550
Main Authors: Qin, Q., Dou, J., Tu, Z.
Format: Article
Language:English
Subjects:
Applied Problems
Computer Science
Deep learning
Discrete Wavelet Transform
Domains
Image Processing and Computer Vision
Image reconstruction
Image resolution
Pattern Recognition
Remote sensing
Wavelet transforms
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Summary:We present a single-image super-resolution (SR) method for Remote Sensing Image based on deep learning within Discrete Wavelet Domain in this paper. Our method is inspired Residual Learning. Firstly, an input image is decomposed by single level 2D Discrete wavelet transform to get four sub-bands. The four sub-bands coefficients are feeding into the Deep Learning Residual Network to predict correspondingly residual images; Adding four sub-band images and residual images as the new sub-bands of 2D wavelet transform; Finally, uses the inverse 2D Discrete wavelet transform to get the final output Super Resolution HR image. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.
ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661820030232