GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling

27 May 2019 Dong-Wook Kim Jae Ryun Chung Seung-Won Jung

Recent research on image denoising has progressed with the development of deep learning architectures, especially convolutional neural networks. However, real-world image denoising is still very challenging because it is not possible to obtain ideal pairs of ground-truth images and real-world noisy images... (read more)

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Datasets


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Color Image Denoising NTIRE 2019 Real Image Denoising Challenge (sRGB) GRDN PSNR 39.931743 # 1
SSIM 0.973589 # 1

Methods used in the Paper


METHOD TYPE
Convolution
Convolutions
Concatenated Skip Connection
Skip Connections
Batch Normalization
Normalization
ReLU
Activation Functions
Dense Block
Image Model Blocks