Super-resolution reconstruction of single remote sensing images based on residual channel attention

Gao, Li; Sun, Hong-Mei; Cui, Zhe; Du, Yan-Bin; Sun, Hai‐Wei; Jia, Rui-Sheng

doi:10.1117/1.jrs.15.016513

Cited by 4 publications

(2 citation statements)

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“…In the same year, Li et al presented an attention-based GAN (SRAGAN) [38] which uses local attention and global attention to capture the detailed features of the earth's surface and the correlation features between channels and spatial dimensions, respectively. In addition, Gao et al presented a remote sensing image SR method that combines residual channel attention (CA) [39]. This network uses the CA module to extract deep feature information from remote sensing images, which can reconstruct images with more precise edges.…”

Section: Related Workmentioning

confidence: 99%

TDEGAN: A Texture-Detail-Enhanced Dense Generative Adversarial Network for Remote Sensing Image Super-Resolution

Guo,

Xiong,

Zhao

et al. 2024

Remote Sensing

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Image super-resolution (SR) technology can improve the resolution of images and provide clearer and more reliable remote sensing images of high quality to better serve the subsequent applications. However, when reconstructing high-frequency feature areas of remote sensing images, existing SR reconstruction methods are prone to artifacts that affect visual effects and make it difficult to generate real texture details. In order to address this issue, a texture-detail-enhanced dense generative adversarial network (TDEGAN) for remote sensing image SR is presented. The generator uses multi-level dense connections, residual connections, and Shuffle attention (SA) to improve the feature extraction ability. A PatchGAN-style discrimination network is designed to effectively perform local discrimination and helps the network generate rich, detailed features. To reduce the impact of artifacts, we introduce an artifact loss function, which is combined with the exponential moving average (EMA) technique to distinguish the artifacts generated from the actual texture details through local statistics, which can help the network reduce artifacts and generate more realistic texture details. Experiments show that TDEGAN can better restore the texture details of remote sensing images and achieves certain advantages in terms of evaluation indicators and visualization.

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Section: Related Workmentioning

confidence: 99%

TDEGAN: A Texture-Detail-Enhanced Dense Generative Adversarial Network for Remote Sensing Image Super-Resolution

Guo,

Xiong,

Zhao

et al. 2024

Remote Sensing

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show abstract

“…The Ref. [41] proposes a technique with the combination of residual channel attention (CA) to extract deep features. They combine shallow and deep futures, using skip connections to improve the model training.…”

Section: Deep Learning and Super-resolution (Sr) Techniquesmentioning

confidence: 99%

A Novel Implicit Neural Representation for Volume Data

Sheibanifard

2023

Applied Sciences

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The storage of medical images is one of the challenges in the medical imaging field. There are variable works that use implicit neural representation (INR) to compress volumetric medical images. However, there is room to improve the compression rate for volumetric medical images. Most of the INR techniques need a huge amount of GPU memory and a long training time for high-quality medical volume rendering. In this paper, we present a novel implicit neural representation to compress volume data using our proposed architecture, that is, the Lanczos downsampling scheme, SIREN deep network, and SRDenseNet high-resolution scheme. Our architecture can effectively reduce training time, and gain a high compression rate while retaining the final rendering quality. Moreover, it can save GPU memory in comparison with the existing works. The experiments show that the quality of reconstructed images and training speed using our architecture is higher than current works which use the SIREN only. Besides, the GPU memory cost is evidently decreased.

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Advancements in Deep Learning-Based Super-resolution for Remote Sensing: A Comprehensive Review and Future Directions

Hosseini Tabesh,

Babadi Ataabadi,

Chen

2024

Unsupervised and Semi-Supervised Learning

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Super-resolution reconstruction of single remote sensing images based on residual channel attention

Cited by 4 publications

References 0 publications

TDEGAN: A Texture-Detail-Enhanced Dense Generative Adversarial Network for Remote Sensing Image Super-Resolution

TDEGAN: A Texture-Detail-Enhanced Dense Generative Adversarial Network for Remote Sensing Image Super-Resolution

A Novel Implicit Neural Representation for Volume Data

Advancements in Deep Learning-Based Super-resolution for Remote Sensing: A Comprehensive Review and Future Directions

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