Abstract
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. Experiments on benchmark datasets with
different degradation models show that our RDN achieves
favorable performance against state-of-the-art methods.