PSNR: 35.28SSIM: 0.9345
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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: 35.19SSIM: 0.9335
PSNR: 35.32SSIM: 0.9348
PSNR: 34.7SSIM: 0.9302
PSNR: 35.21
PSNR: 33.75
PSNR: 34.86SSIM: 0.9307
PSNR: 34.729
PSNR: 34.70
PSNR: 35.2SSIM: 0.938
PSNR: 35.35SSIM: 0.9347
PSNR: 33.82SSIM: 0.923
PSNR: 33.43
PSNR: 34.74SSIM: 0.9300
PSNR: 33.99
PSNR: 34.04SSIM: 0.9233
PSNR: 34.36
PSNR: 34.89
PSNR: 34.34
PSNR: 34.17
PSNR: 33.84
PSNR: 35.13SSIM: 0.9328
PSNR: 34.69SSIM: 0.9296
PSNR: 34.65SSIM: 0.9289
PSNR: 34.85SSIM: 0.9300