Dynamic proximal unrolling network for compressive imaging

is developed, which integrates a dynamic deblocking module and reconstructs jointly with DPUNet to further improve the performance. Experimental results demonstrate that the proposed method can effectively handle multiple compressive imaging modalities under varying sampling ratios and noise levels via only one trained model, and outperform the state-of-the-art approaches. Our code is available at https://github.com/Yixiao-Yang/DPUNet-PyTorch.