1 research outputs found
Cascaded Reconstruction Network for Compressive image sensing
The theory of compressed sensing (CS) has been successfully applied to image
compression in the past few years, whose traditional iterative reconstruction
algorithm is time-consuming. However, it has been reported deep learning-based
CS reconstruction algorithms could greatly reduce the computational complexity.
In this paper, we propose two efficient structures of cascaded reconstruction
networks corresponding to two different sampling methods in CS process. The
first reconstruction network is a compatibly sampling reconstruction network
(CSRNet), which recovers an image from its compressively sensed measurement
sampled by a traditional random matrix. In CSRNet, deep reconstruction network
module obtains an initial image with acceptable quality, which can be further
improved by residual network module based on convolutional neural network. The
second reconstruction network is adaptively sampling reconstruction network
(ASRNet), by matching automatically sampling module with corresponding residual
reconstruction module. The experimental results have shown that the proposed
two reconstruction networks outperform several state-of-the-art compressive
sensing reconstruction algorithms. Meanwhile, the proposed ASRNet can achieve
more than 1 dB gain, as compared with the CSRNet.Comment: 17 pages,16 figure