PSNR: 28.06SSIM: 0.7532
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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: 28.07
PSNR: 27.79
PSNR: 28.05SSIM: 0.7534
PSNR: 27.66SSIM: 0.7380
PSNR: 27.72SSIM: 0.740
PSNR: 27.87SSIM: 0.7453
PSNR: 28.06SSIM: 0.7533
PSNR: 27.71SSIM: 0.7420
PSNR: 27.85SSIM: 0.7455
PSNR: 27.58SSIM: 0.7349
PSNR: 27.72SSIM: 0.7409
PSNR: 27.74SSIM: 0.7421
PSNR: 27.77SSIM: 0.743
PSNR: 27.77SSIM: 0.7436
PSNR: 27.82SSIM: 0.743
PSNR: 27.8SSIM: 0.7439
PSNR: 27.57SSIM: 0.7353
PSNR: 27.86
PSNR: 27.56SSIM: 0.7350
PSNR: 27.62SSIM: 0.7355
PSNR: 27.58SSIM: 0.762MOS: 2.29
PSNR: 27.83
PSNR: 27.72SSIM: 0.7419
PSNR: 27.76SSIM: 0.7441
PSNR: 27.86SSIM: 0.7457
PSNR: 27.85SSIM: 0.7454
PSNR: 27.87SSIM: 0.764