  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.
