World News, Special Report, Americas

[kinoteatrzarya]

The code and models will be released to the public. In this paper, we focus on the decoder part, and specifically, using deep learning for the VCS reconstruction. In general, VCS reconstruction belongs to the ill-posed inverse problem due to the fact that there are more unknown parameters (the desired video) to estimate than the known ones (the compressed measurement). Due to its efficiency and effectiveness, deep learning has recently been the dominant approach for VCS reconstruction. Single stage model takes the measurement (and optionally masks) as input and outputs the desired video directly. Most of the models are only suitable for the masks which are utilized for training. Moreover, as the spatial size changes, the quality of the reconstruction results will also decline. For some of the models, training is limited due to the memory of GPU. Nevertheless, PnP based methods are still time consuming owing to the iterative nature. Combining the above considerations, in this paper, we design a flexible and concise two-stage deep unfolding network, in which we fuse the reference measurement frames (RMF) into each stage.




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