Abstract
Deep unfolding network (DUN) that unfolds the optimization algorithm into a deep neural network has achieved great success in compressive sensing (CS) due to its good interpretability and high performance. Each stage in DUN corresponds to one iteration in optimization. At the test time, all the sampling images generally need to be processed by all stages, which comes at a price of computation burden and is also unnecessary for the images whose contents are easier to restore. In this paper, we focus on CS reconstruction and propose a novel Dynamic Path-Controllable Deep Unfolding Network (DPC-DUN). DPC-DUN with our designed path-controllable selector can dynamically select a rapid and appropriate route for each image and is slimmable by regulating different performance-complexity tradeoffs. Extensive experiments show that our DPC-DUN is highly flexible and can provide excellent performance and dynamic adjustment to get a suitable tradeoff, thus addressing the main requirements to become appealing in practice. Codes are available at this https URL.
Abstract (translated)
展开优化算法并将其展开成深度神经网络的 Deep Un unfold Network (DUN) 在压缩感知(CS)方面取得了巨大的成功,因为其良好的解释性和高性能。DUN 的每一阶段对应着优化中的迭代。在测试时,通常需要对所有采样图像进行处理,这需要计算负担,而对于更容易恢复其内容的图像则没有必要。在本文中,我们专注于 CS 重建并提出了一种新型的动态路径控制 Deep Un unfold Network (DPC-DUN)。DPC-DUN 使用我们设计的可控制路径选择器可以动态地选择每个图像的迅速且适当的路径,并通过调节不同的性能-复杂性权衡来减小规模。广泛的实验表明,我们的 DPC-DUN 非常灵活,可以提供出色的性能和动态调整,以获得适当的权衡,从而满足了在实践中变得吸引人的主要要求。代码可在 this https URL 上获取。
URL
https://arxiv.org/abs/2306.16060
