论文标题
两个阶段就足够了：简洁的深度展开的重建网络，用于灵活的视频压缩感
Two-Stage is Enough: A Concise Deep Unfolding Reconstruction Network for Flexible Video Compressive Sensing
论文作者
论文摘要
我们考虑在深度展开/滚动结构下的视频压缩感测（VC）的重建问题。但是，我们旨在使用最低阶段建立灵活而简洁的模型。与用于反问题的现有深层展开网络不同，在这些网络中，更多的阶段用于更高的性能，但没有针对不同的遮罩和尺度的灵活性，因此我们表明，一个2阶段的深层展开网络可以导致最先进的（SOTA）结果（SOTA）结果（在单级模型中，PSNR在VC中的PSNR中有1.7 db的增长，RevSci，RevSci）。提出的方法具有适应新面具的特性，并随时可以扩展到大型数据，而无需任何其他培训，这要归功于深度展开的优势。此外，我们扩展了颜色VC进行关节重建和示例化的拟议模型。实验结果表明，我们的2阶段模型也已在颜色VCS重建方面实现了SOTA，导致PSNR的> 2.3dB增益比以前的SOTA算法基于插件的框架，同时使重建速度加快了17次。此外，我们发现我们的网络也可以灵活地适用于颜色VCS重建的掩码调制和比例尺大小，因此可以将单个训练有素的网络应用于不同的硬件系统。代码和模型将发布给公众。
We consider the reconstruction problem of video compressive sensing (VCS) under the deep unfolding/rolling structure. Yet, we aim to build a flexible and concise model using minimum stages. Different from existing deep unfolding networks used for inverse problems, where more stages are used for higher performance but without flexibility to different masks and scales, hereby we show that a 2-stage deep unfolding network can lead to the state-of-the-art (SOTA) results (with a 1.7dB gain in PSNR over the single stage model, RevSCI) in VCS. The proposed method possesses the properties of adaptation to new masks and ready to scale to large data without any additional training thanks to the advantages of deep unfolding. Furthermore, we extend the proposed model for color VCS to perform joint reconstruction and demosaicing. Experimental results demonstrate that our 2-stage model has also achieved SOTA on color VCS reconstruction, leading to a >2.3dB gain in PSNR over the previous SOTA algorithm based on plug-and-play framework, meanwhile speeds up the reconstruction by >17 times. In addition, we have found that our network is also flexible to the mask modulation and scale size for color VCS reconstruction so that a single trained network can be applied to different hardware systems. The code and models will be released to the public.
