PSNR: 39.26SSIM: 0.9786
Papers with Code · CC BY-SA 4.0
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: 40.59SSIM: 0.9816
PSNR: 39.28SSIM: 0.977
PSNR: 39.75SSIM: 0.9792
PSNR: 39.3SSIM: 0.979
PSNR: 39.62SSIM: 0.9787
PSNR: 40.71SSIM: 0.9819
PSNR: 25.74SSIM: 0.715
PSNR: 26.04
PSNR: 40.41SSIM: 0.9814
Loss: 99.53
PSNR: 30.02SSIM: 0.816
PSNR: 39.08
PSNR: 28.72SSIM: 0.8514
PSNR: 28.54
PSNR: 27.25SSIM: 0.786
PSNR: 39.37SSIM: 0.9785
PSNR: 37.93SSIM: 0.9746
PSNR: 38.88
PSNR: 39.15SSIM: 0.9781
PSNR: 40.61SSIM: 0.9816