ISTA-Net++: Flexible Deep Unfolding Network for Compressive Sensing
This addresses practical challenges in image compressive sensing for applications requiring adaptability to varying compression ratios and scenes, though it is incremental as it builds on existing unfolding networks.
The paper tackles the lack of flexibility in deep neural networks for image compressive sensing (CS) across multi-ratio tasks and multi-scene images, proposing ISTA-Net++ which achieves state-of-the-art results on four datasets with superior performance and strong flexibility.
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.