PSNR: 29.29SSIM: 0.7983
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: 29.28SSIM: 0.7974
PSNR: 29.12SSIM: 0.7941
PSNR: 29.28SSIM: 0.7979
PSNR: 28.94
PSNR: 29.08SSIM: 0.7925
PSNR: 29.03SSIM: 0.791
PSNR: 29.02SSIM: 0.7914
PSNR: 28.96SSIM: 0.7914
PSNR: 28.99SSIM: 0.7917
Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution
PSNR: 28.85SSIM: 0.7872
PSNR: 28.87SSIM: 0.7880
PSNR: 28.98SSIM: 0.7904
PSNR: 28.87SSIM: 0.7889
PSNR: 28.94SSIM: 0.789
PSNR: 28.95SSIM: 0.7888
PSNR: 29.06
PSNR: 29.20SSIM: 0.7961
PSNR: 29.29SSIM: 0.7978
PSNR: 28.95
PSNR: 28.92SSIM: 0.7892
PSNR: 29.05SSIM: 0.7921
PSNR: 28.99SSIM: 0.7907
PSNR: 29.15SSIM: 0.7958
PSNR: 29.24SSIM: 0.7974