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    Enhancing the Spatial Resolution of Sentinel-2 Images Through Super-Resolution Using Transformer-Based Deep-Learning Models by Alireza Sharifi, Mohammad Mahdi Safari

    Published 2025-01-01
    “…Experimental results demonstrate superior performance, achieving a peak signal-to-noise ratio (PSNR) of 33.52 dB, structural similarity index (SSIM) of 0.862, and signal-to-reconstruction error ratio (SRE) of 36.7 dB on Sentinel-2 RGB bands. The proposed method outperforms state-of-the-art approaches, including ResNet, Swin Transformer, and ViT, on benchmark datasets (Sentinel-2, AID, and UC-Merced). …”
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