PSNR: 28.84SSIM: 0.7873
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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: 28.44SSIM: 0.7772
PSNR: 28.53SSIM: 0.7895
PSNR: 28.80SSIM: 0.7856
PSNR: 28.82SSIM: 0.7870
PSNR: 28.47SSIM: 0.7720
PSNR: 28.80SSIM: 0.7866
PSNR: 28.82SSIM: 0.786
PSNR: 28.78SSIM: 0.7858
PSNR: 28.80SSIM: 0.7876
PSNR: 28.42SSIM: 0.7774
PSNR: 28.60SSIM: 0.7806
PSNR: 28.81SSIM: 0.7868
PSNR: 28.84SSIM: 0.7888
PSNR: 28.67SSIM: 0.785
PSNR: 28.38SSIM: 0.7760
PSNR: 28.35SSIM: 0.777
PSNR: 28.56SSIM: 0.7803
PSNR: 28.58
PSNR: 28.54SSIM: 0.7800
PSNR: 28.41SSIM: 0.7816
PSNR: 28.36SSIM: 0.7745
PSNR: 28.49SSIM: 0.8184MOS: 2.98
PSNR: 28.81SSIM: 0.7871
PSNR: 28.60SSIM: 0.7813
PSNR: 28.66SSIM: 0.7834
PSNR: 28.73SSIM: 0.7842