Dense Deep Unfolding Network With 3D-CNN Prior for Snapshot Compressive Imaging

Zhuoyuan Wu, Jian Zhang, Chong Mou; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4892-4901

Abstract


Snapshot compressive imaging (SCI) aims to record three-dimensional signals via a two-dimensional camera. For the sake of building a fast and accurate SCI recovery algorithm, we incorporate the interpretability of model-based methods and the speed of learning-based ones and present a novel dense deep unfolding network (DUN) with 3D-CNN prior for SCI, where each phase is unrolled from an iteration of Half-Quadratic Splitting (HQS). To better exploit the spatial-temporal correlation among frames and address the problem of information loss between adjacent phases in existing DUNs, we propose to adopt the 3D-CNN prior in our proximal mapping module and develop a novel dense feature map (DFM) strategy, respectively. Besides, in order to promote network robustness, we further propose a dense feature map adaption (DFMA) module to allow inter-phase information to fuse adaptively. All the parameters are learned in an end-to-end fashion. Extensive experiments on simulation data and real data verify the superiority of our method. The source code is available at \href https://github.com/jianzhangcs/SCI3D https://github.com/jianzhangcs/SCI3D .

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Wu_2021_ICCV, author = {Wu, Zhuoyuan and Zhang, Jian and Mou, Chong}, title = {Dense Deep Unfolding Network With 3D-CNN Prior for Snapshot Compressive Imaging}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {4892-4901} }