Deeply-Recursive Convolutional Network for Image Super-Resolution

CVPR 2016 Jiwon KimJung Kwon LeeKyoung Mu Lee

We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Our network has a very deep recursive layer (up to 16 recursions)... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Image Super-Resolution BSD100 - 2x upscaling DRCN [[Kim et al.2016b]] PSNR 31.85 # 13
Image Super-Resolution BSD100 - 4x upscaling DRCN PSNR 27.21 # 27
SSIM 0.7493 # 5
MOS 2.12 # 3
Image Super-Resolution Set14 - 2x upscaling DRCN [[Kim et al.2016b]] PSNR 33.04 # 11
Image Super-Resolution Set14 - 4x upscaling DRCN PSNR 28.02 # 31
SSIM 0.8074 # 3
MOS 2.84 # 3
Image Super-Resolution Set5 - 2x upscaling DRCN [[Kim et al.2016b]] PSNR 37.63 # 11
Image Super-Resolution Set5 - 4x upscaling DRCN PSNR 31.52 # 24
SSIM 0.8938 # 17
MOS 3.26 # 3
Image Super-Resolution Urban100 - 2x upscaling DRCN [[Kim et al.2016b]] PSNR 30.75 # 10

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet