PSNR: 25.94SSIM: 0.6935MOS: 1.47
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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: 27.12SSIM: 0.7211
PSNR: 25.93SSIM: 0.6813LPIPS: 0.1385DISTS: 0.1072
PSNR: 27.29SSIM: 0.7253
PSNR: 27.32SSIM: 0.728
PSNR: 26.5707SSIM: 0.6900
PSNR: 27.21SSIM: 0.7493MOS: 2.12
PSNR: 27.50SSIM: 0.7326
PSNR: 27.41SSIM: 0.7297
PSNR: 26.38SSIM: 0.738LPIPS: 0.3433
PSNR: 27.4SSIM: 0.729
PSNR: 27.21
PSNR: 27.40SSIM: 0.7290
PSNR: 26.9SSIM: 0.7101
PSNR: 27.25SSIM: 0.724
PSNR: 26.91
PSNR: 27.44SSIM: 0.7302
PSNR: 26.87
PSNR: 27.49SSIM: 0.734
PSNR: 27.42
PSNR: 27.56
PSNR: 27.40SSIM: 0.7281
PSNR: 27.48SSIM: 0.7306
PSNR: 27.02SSIM: 0.7442MOS: 2.01
PSNR: 25.33SSIM: 0.651
PSNR: 27.42SSIM: 0.737
PSNR: 27.16
PSNR: 25.505SSIM: 0.6576LPIPS: 0.1611