PSNR: 31.66SSIM: 0.9196
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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.
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PSNR: 31.17SSIM: 0.9176
PSNR: 27.90SSIM: 0.8610
PSNR: 28.83SSIM: 0.8870
PSNR: 30.061SSIM: 0.888LPIPS: 0.0461DISTS: 0.0328
PSNR: 31.74SSIM: 0.921
SSIM: 0.914
PSNR: 35.17SSIM: 0.9516
PSNR: 31.02SSIM: 0.9148
PSNR: 30.40SSIM: 0.9082
PSNR: 31.15SSIM: 0.9160
PSNR: 31.24SSIM: 0.9174
PSNR: 31.57SSIM: 0.92
PSNR: 35.24SSIM: 0.9513
PSNR: 27.58SSIM: 0.8555
PSNR: 31.22SSIM: 0.9173
PSNR: 31.43SSIM: 0.9201
PSNR: 29.76SSIM: 0.8979
PSNR: 30.45
PSNR: 29.42SSIM: 0.8942
PSNR: 28.08SSIM: 0.8554LPIPS: 0.0524DISTS: 0.0351
PSNR: 31.59
PSNR: 31.0SSIM: 0.9151
PSNR: 31.41SSIM: 0.9187
PSNR: 31.07SSIM: 0.9144
PSNR: 31.73SSIM: 0.9207
PSNR: 33.03
PSNR: 30.72SSIM: 0.9111