论文标题
自适应和级联压缩感
Adaptive and Cascaded Compressive Sensing
论文作者
论文摘要
依赖场景的自适应压缩感（CS）一直是一个漫长的追求目标，在显着提高CS的性能方面具有巨大的潜力。但是，在没有访问地面真相图像的情况下，如何设计与场景有关的自适应策略仍然是一个开放的问题，并且提高采样效率的提高仍然非常有限。在本文中，提出了基于限制的等轴测属性（RIP）误差夹具，该误差夹具可以直接预测重建误差，即当前阶段的重建图像与地面真相图像之间的差异，并将样品自适应地分配给连续采样阶段的不同区域。此外，我们提出了一个级联的特征融合重建网络，该网络可以有效地利用从不同自适应采样阶段得出的信息。与最先进的CS算法相比，提出的自适应和级联CS方法的有效性通过广泛的定量和定性结果证明。
Scene-dependent adaptive compressive sensing (CS) has been a long pursuing goal which has huge potential in significantly improving the performance of CS. However, without accessing to the ground truth image, how to design the scene-dependent adaptive strategy is still an open-problem and the improvement in sampling efficiency is still quite limited. In this paper, a restricted isometry property (RIP) condition based error clamping is proposed, which could directly predict the reconstruction error, i.e. the difference between the currently-stage reconstructed image and the ground truth image, and adaptively allocate samples to different regions at the successive sampling stage. Furthermore, we propose a cascaded feature fusion reconstruction network that could efficiently utilize the information derived from different adaptive sampling stages. The effectiveness of the proposed adaptive and cascaded CS method is demonstrated with extensive quantitative and qualitative results, compared with the state-of-the-art CS algorithms.
