  Deep Learning (DL) based Compressed Sensing (CS) has been applied for better
performance of image reconstruction than traditional CS methods. However, most
existing DL methods utilize the block-by-block measurement and each measurement
block is restored separately, which introduces harmful blocking effects for
reconstruction. Furthermore, the neuronal receptive fields of those methods are
designed to be the same size in each layer, which can only collect single-scale
spatial information and has a negative impact on the reconstruction process.
This paper proposes a novel framework named Multi-scale Dilated Convolution
Neural Network (MsDCNN) for CS measurement and reconstruction. During the
measurement period, we directly obtain all measurements from a trained
measurement network, which employs fully convolutional structures and is
jointly trained with the reconstruction network from the input image. It
needn't be cut into blocks, which effectively avoids the block effect. During
the reconstruction period, we propose the Multi-scale Feature Extraction (MFE)
architecture to imitate the human visual system to capture multi-scale features
from the same feature map, which enhances the image feature extraction ability
of the framework and improves the performance of image reconstruction. In the
MFE, there are multiple parallel convolution channels to obtain multi-scale
feature information. Then the multi-scale features information is fused and the
original image is reconstructed with high quality. Our experimental results
show that the proposed method performs favorably against the state-of-the-art
methods in terms of PSNR and SSIM.
