Deep learning-based compressive sensing (DCS) has improved the compressive sensing (CS) with fast and high reconstruction quality. Researchers have further extended it to multi-scale DCS which improves reconstruction quality based on Wavelet decomposition. In this work, we mimic the Difference of Gaussian via convolution and propose a scheme named as Difference of convolution-based multi-scale DCS (DoC-DCS). Unlike the multi-scale DCS based on a well-designed filter in wavelet domain, the proposed DoC-DCS learns decomposition, thereby, outperforms other state-of-the-art compressive sensing methods.
Please cite this paper if you use the source code
@article{Canh2019_DoCDCS,
title={Difference of Convolution for Deep Compressive Sensing},
author={Thuong, Nguyen Canh and Byeungwoo, Jeon},
conference={IEEE International Conference on Image Processing},
year={2019}
}
Training code with three phase training is added. Dataset is created follow this DnCNN repo.
several test set in comparison with MS-DCI
512x512 images
In general, DoC-DCS has better quality than our MS-DCI paper (TCI-2020)
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