Enhanced Deep Residual Networks for Single Image Super-Resolution

10 Jul 2017Bee LimSanghyun SonHeewon KimSeungjun NahKyoung Mu Lee

Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Image Super-Resolution BSD100 - 4x upscaling EDSR PSNR 27.71 # 7
Image Super-Resolution BSD100 - 4x upscaling EDSR SSIM 0.7420 # 9
Image Super-Resolution Manga109 - 4x upscaling EDSR PSNR 31.02 # 5
Image Super-Resolution Manga109 - 4x upscaling EDSR SSIM 0.9148 # 7
Image Super-Resolution Set14 - 4x upscaling EDSR PSNR 28.80 # 9
Image Super-Resolution Set14 - 4x upscaling EDSR SSIM 0.7876 # 10
Image Super-Resolution Set5 - 4x upscaling EDSR PSNR 32.46 # 7
Image Super-Resolution Set5 - 4x upscaling EDSR SSIM 0.8968 # 11
Image Super-Resolution Urban100 - 4x upscaling EDSR PSNR 26.64 # 6
Image Super-Resolution Urban100 - 4x upscaling EDSR SSIM 0.8033 # 5