PSNR: 28.01SSIM: 0.7651
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Image Super-Resolution
Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
논문
PSNR: 27.421SSIM: 0.7438LPIPS: 0.1027DISTS: 0.0819
PSNR: 28.04SSIM: 0.7672
PSNR: 28.19SSIM: 0.772
PSNR: 27.6222SSIM: 0.7419
PSNR: 26.22
PSNR: 28.02SSIM: 0.8074MOS: 2.84
PSNR: 27.63
PSNR: 27.57SSIM: 0.76
PSNR: 25.19SSIM: 0.894
PSNR: 28.30SSIM: 0.7736
PSNR: 28.09SSIM: 0.782
PSNR: 28.25SSIM: 0.773
PSNR: 27.86SSIM: 0.7718
PSNR: 27.5SSIM: 0.7513
PSNR: 28.07SSIM: 0.770
PSNR: 27.48
PSNR: 28.29SSIM: 0.7741
PSNR: 27.37
PSNR: 27.59
PSNR: 28.26SSIM: 0.7723
PSNR: 25.99SSIM: 0.7397MOS: 3.72
PSNR: 27.66SSIM: 0.8004MOS: 2.52
PSNR: 26.13SSIM: 0.694
PSNR: 27.72SSIM: 0.7405
PSNR: 28.32SSIM: 0.784
PSNR: 27.88
PSNR: 27.83SSIM: 0.7631
PSNR: 26.64SSIM: 0.7930
PSNR: 27.68