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 https://github.com/songjiechong/DPC-DUN.

翻译：深度展开网络（DUN）将优化算法展开为深度神经网络，因其良好的可解释性和高性能在压缩感知（CS）中取得了巨大成功。DUN中的每个阶段对应优化中的一次迭代。在测试时，所有采样图像通常需要经过所有阶段处理，这带来了计算负担，且对于内容易于恢复的图像而言也并非必要。本文聚焦于CS重构问题，提出了一种新颖的动态路径可控深度展开网络（DPC-DUN）。我们设计的路径可控选择器使DPC-DUN能够为每张图像动态选择快速且合适的路径，并通过调节不同的性能-复杂度权衡实现模型瘦身。大量实验表明，我们的DPC-DUN具有高度灵活性，能够提供优异的性能并进行动态调整以获得合适的权衡，从而满足实际应用中的主要需求。代码已开源在https://github.com/songjiechong/DPC-DUN。