Deep learning-based compressive sensing (DCS) has improved the single scale 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 the wavelet domain, our DoC-DCS jointly learns decomposition, sampling, and reconstruction thereby outperforms other state-of-the-art deep learning based CS methods.