PSNR: 26.62SSIM: 0.802
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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.
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PSNR: 26.148SSIM: 0.7884LPIPS: 0.0945DISTS: 0.0742
PSNR: 25.2SSIM: 0.7521
PSNR: 26.42SSIM: 0.7940
PSNR: 25.21SSIM: 0.756
PSNR: 25.66SSIM: 0.7703
PSNR: 26.04SSIM: 0.785
PSNR: 25.41SSIM: 0.7632
PSNR: 26.07SSIM: 0.7837
PSNR: 26.606
PSNR: 26.6SSIM: 0.8015
PSNR: 25.5
PSNR: 24.52SSIM: 0.7221
PSNR: 25.08SSIM: 0.747
PSNR: 25.59SSIM: 0.7680
PSNR: 25.68SSIM: 0.773
PSNR: 26.03SSIM: 0.7835
PSNR: 26.04
PSNR: 26.05SSIM: 0.7834
PSNR: 25.50SSIM: 0.7630
PSNR: 26.53SSIM: 0.8019
PSNR: 26.27SSIM: 0.7890
PSNR: 23.28SSIM: 0.7517
PSNR: 25.79SSIM: 0.7729
PSNR: 24.33SSIM: 0.7707LPIPS: 0.1065DISTS: 0.0764
PSNR: 26.61SSIM: 0.8028
PSNR: 25.42SSIM: 0.771
PSNR: 24.799SSIM: 0.9481Perceptual Index: 3.5511LPIPS: 0.1184