  While deep neural networks have achieved impressive success in image
compressive sensing (CS), most of them lack flexibility when dealing with
multi-ratio tasks and multi-scene images in practical applications. To tackle
these challenges, we propose a novel end-to-end flexible ISTA-unfolding deep
network, dubbed ISTA-Net++, with superior performance and strong flexibility.
Specifically, by developing a dynamic unfolding strategy, our model enjoys the
adaptability of handling CS problems with different ratios, i.e., multi-ratio
tasks, through a single model. A cross-block strategy is further utilized to
reduce blocking artifacts and enhance the CS recovery quality. Furthermore, we
adopt a balanced dataset for training, which brings more robustness when
reconstructing images of multiple scenes. Extensive experiments on four
datasets show that ISTA-Net++ achieves state-of-the-art results in terms of
both quantitative metrics and visual quality. Considering its flexibility,
effectiveness and practicability, our model is expected to serve as a suitable
baseline in future CS research. The source code is available on
https://github.com/jianzhangcs/ISTA-Netpp.
