PSNR: 29.63SSIM: 0.8191
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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: 29.59SSIM: 0.8177
PSNR: 29.6
PSNR: 28.85
PSNR: 29.4SSIM: 0.8125
PSNR: 24.25SSIM: 0.851
PSNR: 29.24
PSNR: 24.77SSIM: 0.6817LPIPS: 0.1572
PSNR: 29.67SSIM: 0.8256
PSNR: 29.66SSIM: 0.8196
PSNR: 28.93SSIM: 0.7994
PSNR: 28.76
PSNR: 29.33SSIM: 0.8105
PSNR: 28.87
PSNR: 28.91
PSNR: 29.09
PSNR: 29.39
PSNR: 29.12
LPIPS: 0.1209
PSNR: 24.87SSIM: 0.6869LPIPS: 0.1500
PSNR: 24.95SSIM: 0.6317
Normalized cPSNR: 0.9313717296835213
SSIM: 0.7515
PSNR: 29.27SSIM: 0.8111
PSNR: 29.41SSIM: 0.8116
Normalized cPSNR: 0.9300466898779116
SSIM: 0.7605