MemNet: A Persistent Memory Network for Image Restoration

Recently, very deep convolutional neural networks (CNNs) have been attracting considerable attention in image restoration. However, as the depth grows, the long-term dependency problem is rarely realized for these very deep models, which results in the prior states/layers having little influence on the subsequent ones... (read more)

PDF Abstract ICCV 2017 PDF ICCV 2017 Abstract

Datasets


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Super-Resolution BSD100 - 4x upscaling MemNet PSNR 27.40 # 24
SSIM 0.7281 # 31
Color Image Denoising CBSD68 sigma50 MemNet PSNR 26.33 # 9
JPEG Artifact Correction Classic5 (Quality 10 Grayscale) MemNet PSNR 29.69 # 4
JPEG Artifact Correction LIVE1 (Quality 10 Color) MemNet PSNR 27.33 # 5
PSNR-B 27.34 # 5
SSIM 0.810 # 3
JPEG Artifact Correction Live1 (Quality 10 Grayscale) MemNet PSNR 29.45 # 5
PSNR-B 29.39 # 2
SSIM 0.8327 # 4
JPEG Artifact Correction LIVE1 (Quality 20 Color) MemNet PSNR 29.76 # 5
PSNR-B 29.75 # 4
SSIM 0.877 # 3
JPEG Artifact Correction LIVE1 (Quality 20 Grayscale) MemNet PSNR 31.83 # 5
PSNR-B 31.74 # 4
SSIM 0.8970 # 5
Image Super-Resolution Manga109 - 4x upscaling MemNet PSNR 29.42 # 18
SSIM 0.8942 # 16
Image Super-Resolution Set14 - 4x upscaling MemNet PSNR 28.26 # 27
SSIM 0.7723 # 32
Image Super-Resolution Set5 - 4x upscaling MemNet PSNR 31.74 # 25
SSIM 0.8893 # 28
Image Super-Resolution Urban100 - 4x upscaling MemNet PSNR 25.50 # 27
SSIM 0.7630 # 28

Methods used in the Paper


METHOD TYPE
Memory Network
Working Memory Models