Toward Convolutional Blind Denoising of Real Photographs

CVPR 2019 Shi GuoZifei YanKai ZhangWangmeng ZuoLei Zhang

While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs. The main reason is that their learned models are easy to overfit on the simplified AWGN model which deviates severely from the complicated real-world noise model... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Color Image Denoising Darmstadt Noise Dataset CBDNet (Blind) PSNR (sRGB) 38.06 # 4
SSIM (sRGB) 0.9421 # 4
Denoising Darmstadt Noise Dataset CBDNet(Syn) PSNR 37.57 # 3