  Most traditional algorithms for compressive sensing image reconstruction
suffer from the intensive computation. Recently, deep learning-based
reconstruction algorithms have been reported, which dramatically reduce the
time complexity than iterative reconstruction algorithms. In this paper, we
propose a novel \textbf{D}eep \textbf{R}esidual \textbf{R}econstruction Network
(DR$^{2}$-Net) to reconstruct the image from its Compressively Sensed (CS)
measurement. The DR$^{2}$-Net is proposed based on two observations: 1) linear
mapping could reconstruct a high-quality preliminary image, and 2) residual
learning could further improve the reconstruction quality. Accordingly,
DR$^{2}$-Net consists of two components, \emph{i.e.,} linear mapping network
and residual network, respectively. Specifically, the fully-connected layer in
neural network implements the linear mapping network. We then expand the linear
mapping network to DR$^{2}$-Net by adding several residual learning blocks to
enhance the preliminary image. Extensive experiments demonstrate that the
DR$^{2}$-Net outperforms traditional iterative methods and recent deep
learning-based methods by large margins at measurement rates 0.01, 0.04, 0.1,
and 0.25, respectively. The code of DR$^{2}$-Net has been released on:
https://github.com/coldrainyht/caffe\_dr2
