Song Jiechong, Chen Bin, Zhang Jian
IEEE Trans Image Process. 2023;32:2202-2214. doi: 10.1109/TIP.2023.3263100. Epub 2023 Apr 13.
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.
深度展开网络（DUN）将优化算法展开为深度神经网络，由于其良好的可解释性和高性能，在压缩感知（CS）领域取得了巨大成功。DUN中的每个阶段对应于优化中的一次迭代。在测试时，所有采样图像通常都需要由所有阶段进行处理，这带来了计算负担，而且对于内容较容易恢复的图像来说也是不必要的。在本文中，我们专注于CS重建，并提出了一种新颖的动态路径可控深度展开网络（DPC-DUN）。带有我们设计的路径可控选择器的DPC-DUN可以为每个图像动态选择一条快速且合适的路径，并通过调节不同的性能-复杂度权衡来实现可瘦身。大量实验表明，我们的DPC-DUN具有高度的灵活性，可以提供出色的性能和动态调整以获得合适的权衡，从而满足在实际应用中具有吸引力的主要要求。代码可在https://github.com/songjiechong/DPC-DUN获取。
