A novel generative adversarial network framework for super-resolution reconstruction of remote sensing
IntroductionRemote sensing super-resolution (RS-SR) plays a crucial role in the analysis of remote sensing images, aiming to improve the spatial resolution of images with lower resolutions. Recent advancements in RS-SR research have been largely driven by the integration of deep learning techniques,...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1578321/full |
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| author | Ruilin Li Linzhi Wen Songtao Shao Ming Yu Linda Mohaisen |
| author_facet | Ruilin Li Linzhi Wen Songtao Shao Ming Yu Linda Mohaisen |
| author_sort | Ruilin Li |
| collection | DOAJ |
| description | IntroductionRemote sensing super-resolution (RS-SR) plays a crucial role in the analysis of remote sensing images, aiming to improve the spatial resolution of images with lower resolutions. Recent advancements in RS-SR research have been largely driven by the integration of deep learning techniques, especially through the application of Generative Adversarial Networks (GANs), which have shown significant effectiveness in advancing this field. While GAN has achieved notable advancements in this field, its tendency toward pattern collapse often introduces artifacts and distorts textures in the reconstructed images.MethodsThis study introduces a novel RS-SR model, termed the Diffusion Enhanced Generative Adversarial Network (DEGAN), designed to improve the quality of RS-SR images through the incorporation of a diffusion model. At the heart of DEGAN lies an innovative GAN architecture that fuses the adversarial mechanisms of both the generator and discriminator with an integrated diffusion module. This additional component utilizes the noise reduction capabilities of the diffusion process to refine the intermediate stages of image generation, ultimately improving the clarity of the final output and enhancing the performance of remote sensing super-resolution.ResultsIn the test dataset, the peak signal-to-noise ratio (PSNR) increased by 0.345 dB at 2× scaling and 0.671 dB at 4× scaling, while the structural similarity index (SSIM) was improved by 0.0087 and 0.0166, respectively, compared to the current state-of-the-art (SOTA) approach.DiscussionThese results indicate that DEGAN significantly improves the super-resolution reconstruction performance of remote sensing images. The introduction of the diffusion module and attention mechanism effectively reduces noise and enhances image clarity, addressing common issues of texture distortion and artifacts in remote sensing image super-resolution reconstruction. |
| format | Article |
| id | doaj-art-3670d3ab68244b6fb3ea47dfbc71e93d |
| institution | OA Journals |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Earth Science |
| spelling | doaj-art-3670d3ab68244b6fb3ea47dfbc71e93d2025-08-20T02:27:13ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-05-011310.3389/feart.2025.15783211578321A novel generative adversarial network framework for super-resolution reconstruction of remote sensingRuilin Li0Linzhi Wen1Songtao Shao2Ming Yu3Linda Mohaisen4College of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaIntroductionRemote sensing super-resolution (RS-SR) plays a crucial role in the analysis of remote sensing images, aiming to improve the spatial resolution of images with lower resolutions. Recent advancements in RS-SR research have been largely driven by the integration of deep learning techniques, especially through the application of Generative Adversarial Networks (GANs), which have shown significant effectiveness in advancing this field. While GAN has achieved notable advancements in this field, its tendency toward pattern collapse often introduces artifacts and distorts textures in the reconstructed images.MethodsThis study introduces a novel RS-SR model, termed the Diffusion Enhanced Generative Adversarial Network (DEGAN), designed to improve the quality of RS-SR images through the incorporation of a diffusion model. At the heart of DEGAN lies an innovative GAN architecture that fuses the adversarial mechanisms of both the generator and discriminator with an integrated diffusion module. This additional component utilizes the noise reduction capabilities of the diffusion process to refine the intermediate stages of image generation, ultimately improving the clarity of the final output and enhancing the performance of remote sensing super-resolution.ResultsIn the test dataset, the peak signal-to-noise ratio (PSNR) increased by 0.345 dB at 2× scaling and 0.671 dB at 4× scaling, while the structural similarity index (SSIM) was improved by 0.0087 and 0.0166, respectively, compared to the current state-of-the-art (SOTA) approach.DiscussionThese results indicate that DEGAN significantly improves the super-resolution reconstruction performance of remote sensing images. The introduction of the diffusion module and attention mechanism effectively reduces noise and enhances image clarity, addressing common issues of texture distortion and artifacts in remote sensing image super-resolution reconstruction.https://www.frontiersin.org/articles/10.3389/feart.2025.1578321/fullsuper-resolutiondiffusion modelgenerative adversarial networkremote sensing image reconstructionattention mechanism |
| spellingShingle | Ruilin Li Linzhi Wen Songtao Shao Ming Yu Linda Mohaisen A novel generative adversarial network framework for super-resolution reconstruction of remote sensing Frontiers in Earth Science super-resolution diffusion model generative adversarial network remote sensing image reconstruction attention mechanism |
| title | A novel generative adversarial network framework for super-resolution reconstruction of remote sensing |
| title_full | A novel generative adversarial network framework for super-resolution reconstruction of remote sensing |
| title_fullStr | A novel generative adversarial network framework for super-resolution reconstruction of remote sensing |
| title_full_unstemmed | A novel generative adversarial network framework for super-resolution reconstruction of remote sensing |
| title_short | A novel generative adversarial network framework for super-resolution reconstruction of remote sensing |
| title_sort | novel generative adversarial network framework for super resolution reconstruction of remote sensing |
| topic | super-resolution diffusion model generative adversarial network remote sensing image reconstruction attention mechanism |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1578321/full |
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