  With joint learning of sampling and recovery, the deep learning-based
compressive sensing (DCS) has shown significant improvement in performance and
running time reduction. Its reconstructed image, however, losses high-frequency
content especially at low subrates. This happens similarly in the multi-scale
sampling scheme which also samples more low-frequency components. In this
paper, we propose a multi-scale DCS convolutional neural network (MS-DCSNet) in
which we convert image signal using multiple scale-based wavelet transform,
then capture it through convolution block by block across scales. The initial
reconstructed image is directly recovered from multi-scale measurements.
Multi-scale wavelet convolution is utilized to enhance the final reconstruction
quality. The network is able to learn both multi-scale sampling and multi-scale
reconstruction, thus results in better reconstruction quality.
