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. In
this paper, we propose a novel residual dense network (RDN) to address this
problem in IR. We fully exploit the hierarchical features from all the
convolutional layers. Specifically, we propose residual dense block (RDB) to
extract abundant local features via densely connected convolutional layers. RDB
further allows direct connections from the state of preceding RDB to all the
layers of current RDB, leading to a contiguous memory mechanism. To adaptively
learn more effective features from preceding and current local features and
stabilize the training of wider network, we proposed local feature fusion in
RDB. After fully obtaining dense local features, we use global feature fusion
to jointly and adaptively learn global hierarchical features in a holistic way.
We demonstrate the effectiveness of RDN with several representative IR
applications, single image super-resolution, Gaussian image denoising, image
compression artifact reduction, and image deblurring. Experiments on benchmark
and real-world datasets show that our RDN achieves favorable performance
against state-of-the-art methods for each IR task quantitatively and visually.

本文提出了一种新的残差密集网络（RDN），该网络从卷积层中充分利用层级特征，引入残余密集块（RDB）来提取丰富的局部特征，并允许直接连接，通过局部特征融合，以自适应地学习全局的分层特征。用于单张超分辨率、高斯图像去噪、图像压缩伪影去除和图像去模糊等多个任务，RDN 在基准和实际数据集上均取得了优异的性能。

图像恢复的残差稠密网络

Residual Dense Network for Image Restoration

A very deep convolutional neural network (CNN) has recently achieved great
success for image super-resolution (SR) and offered hierarchical features as
well. However, most deep CNN based SR models do not make full use of the
hierarchical features from the original low-resolution (LR) images, thereby
achieving relatively-low performance. In this paper, we propose a novel
residual dense network (RDN) to address this problem in image SR. We fully
exploit the hierarchical features from all the convolutional layers.
Specifically, we propose residual dense block (RDB) to extract abundant local
features via dense connected convolutional layers. RDB further allows direct
connections from the state of preceding RDB to all the layers of current RDB,
leading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is
then used to adaptively learn more effective features from preceding and
current local features and stabilizes the training of wider network. After
fully obtaining dense local features, we use global feature fusion to jointly
and adaptively learn global hierarchical features in a holistic way. Extensive
experiments on benchmark datasets with different degradation models show that
our RDN achieves favorable performance against state-of-the-art methods.

本文提出了一种新型的残差稠密网络 (RDN)，该网络通过引入残差稠密块 (RDB) 来充分利用原始低分辨率图像的层级特征，并通过局部特征融合和全局特征融合来稳定更广泛的网络培训，进而在基准数据集上展示了 RDN 相对于现有最先进的方法所取得的更高性能。