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EGDSR: Encoder-Generator-Decoder Network for Remote Sensing Super-Resolution Reconstruction
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DOI：10.1109/LGRS.2023.3316621.png)
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Remote sensing single-image super-resolution (SR) reconstruction process detail information is easy to be lost and prone to defocus phenomenon; in order to solve these problems, we are based on the idea of potential spatial vector mapping, focusing on the attribute control of the feature recovery process, to improve the quality of remote sensing image reconstruction. This letter proposes an encoder-generator-decoder SR reconstruction (SRR) network for remote sensing named EGDSR. We design three modules: multiscale feature extraction and latent code generation module (MS-FCGM), multiattribute control of resolution progression module (MA-RPM) (recovery of latent encoding, fusion of multiscale features, and generation of high-resolution (HR) features), and high-resolution image reconstruction module (HRRM). The experimental results show that the HR images generated by our proposed EGDSR network have a stronger sense of truth, richer texture, and more realistic details, and have a better perceptual effect compared with other state-of-the-art SR networks. In addition, we also combine the semantic segmentation task to assist in verifying the quality of the HR remote sensing images generated by EGDSR, and successfully verify the higher application value of our proposed method.
Keyword:
Latent code recovery
multiscale feature
remote sensing
super-resolution (SR) reconstruction
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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