Residual Dense Network for Image Restoration

25 Dec 2018 Yulun Zhang Yapeng Tian Yu Kong Bineng Zhong Yun Fu

Convolutional neural network has recently achieved great success for image restoration (IR) and also offered hierarchical features. However, most deep CNN based IR models do not make full use of the hierarchical features from the original low-quality images, thereby achieving relatively-low performance... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Color Image Denoising BSD68 sigma10 Residual Dense Network + PSNR 36.49 # 1
Grayscale Image Denoising BSD68 sigma10 Residual Dense Network + PSNR 34.01 # 1
Grayscale Image Denoising BSD68 sigma30 Residual Dense Network + PSNR 28.58 # 1
Color Image Denoising BSD68 sigma30 Residual Dense Network + PSNR 30.7 # 1
Grayscale Image Denoising BSD68 sigma50 Residual Dense Network + PSNR 26.43 # 3
Color Image Denoising BSD68 sigma70 Residual Dense Network + PSNR 26.88 # 1
Grayscale Image Denoising BSD68 sigma70 Residual Dense Network + PSNR 25.12 # 2
JPEG Artifact Correction Classic5 (Quality 10 Grayscale) Residual Dense Network + PSNR 30.03 # 1
SSIM 0.8194 # 2
JPEG Artifact Correction Classic5 (Quality 20 Grayscale) Residual Dense Network + PSNR 32.19 # 1
SSIM 0.8704 # 2
JPEG Artifact Correction Classic5 (Quality 30 Grayscale) Residual Dense Network + PSNR 33.46 # 1
SSIM 0.8932 # 2
JPEG Artifact Correction Classic5 (Quality 40 Grayscale) Residual Dense Network + PSNR 34.29 # 1
SSIM 0.9063 # 1
Grayscale Image Denoising Kodak24 sigma10 Residual Dense Network + PSNR 35.19 # 1
Color Image Denoising Kodak24 sigma10 Residual Dense Network + PSNR 37.33 # 1
Grayscale Image Denoising Kodak24 sigma30 Residual Dense Network + PSNR 30.02 # 1
Color Image Denoising Kodak24 sigma30 Residual Dense Network + PSNR 31.98 # 1
Color Image Denoising Kodak24 sigma50 Residual Dense Network + PSNR 29.7 # 1
Grayscale Image Denoising Kodak24 sigma50 Residual Dense Network + PSNR 27.88 # 1
Grayscale Image Denoising Kodak24 sigma70 Residual Dense Network + PSNR 26.57 # 1
Color Image Denoising Kodak24 sigma70 Residual Dense Network + PSNR 28.24 # 1
JPEG Artifact Correction Live1 (Quality 10 Grayscale) Residual Dense Network + PSNR 29.7 # 2
SSIM 0.8252 # 6
JPEG Artifact Correction LIVE1 (Quality 20 Grayscale) Residual Dense Network + PSNR 32.1 # 1
SSIM 0.8886 # 7
JPEG Artifact Correction LIVE1 (Quality 30 Grayscale) Residual Dense Network + PSNR 33.54 # 1
SSIM 0.9156 # 2
JPEG Artifact Correction LIVE1 (Quality 40 Grayscale) Residual Dense Network + PSNR 34.54 # 1
SSIM 0.9304 # 1
Grayscale Image Denoising Urban100 sigma10 Residual Dense Network + PSNR 35.45 # 1
Color Image Denoising Urban100 sigma10 Residual Dense Network + PSNR 36.75 # 1
Color Image Denoising Urban100 sigma30 Residual Dense Network + PSNR 31.78 # 1
Grayscale Image Denoising Urban100 sigma30 Residual Dense Network + PSNR 30.08 # 1
Grayscale Image Denoising Urban100 sigma50 Residual Dense Network + PSNR 27.47 # 2
Color Image Denoising Urban100 sigma50 Residual Dense Network + PSNR 29.38 # 1
Color Image Denoising Urban100 sigma70 Residual Dense Network + PSNR 27.74 # 1
Grayscale Image Denoising Urban100 sigma70 Residual Dense Network + PSNR 25.71 # 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