# Result for training SR-ResNet network to perform single-image super resolution.

The SR-ResNet mocel is trained on DIV2K dataset and inference is run on BSD test set.

To compare only selected loss functions, tick their names and click submit below. Click on any restored image to see its original version.

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Ground Truth
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Average metric score for the dataset:

PSNR: 23.78
SSIM: 0.604
NIQE: 6.565

PSNR: 24.68
SSIM: 0.695
NIQE: 5.676

PSNR: 24.56
SSIM: 0.692
NIQE: 5.506

PSNR: 23.42
SSIM: 0.609
NIQE: 4.215

PSNR: 25.07
SSIM: 0.692
NIQE: 4.104

PSNR: 24.18
SSIM: 0.669
NIQE: 4.915

PSNR: 24.48
SSIM: 0.67
NIQE: 4.915

PSNR: 24.07
SSIM: 0.695
NIQE: 4.861

PSNR: 23.19
SSIM: 0.608
NIQE: 4.725

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