To more efficiently address image compressed sensing (CS) problems, we present a novel content-aware scalable network dubbed CASNet which collectively achieves adaptive sampling rate allocation, fine granular scalability and high-quality reconstruction. We first adopt a data-driven saliency detector to evaluate the importance of different image regions and propose a saliency-based block ratio aggregation (BRA) strategy for sampling rate allocation. A unified learnable generating matrix is then developed to produce sampling matrix of any CS ratio with an ordered structure. Being equipped with the optimization-inspired recovery subnet guided by saliency information and a multi-block training scheme preventing blocking artifacts, CASNet jointly reconstructs the image blocks sampled at various sampling rates with one single model. To accelerate training convergence and improve network robustness, we propose an SVD-based initialization scheme and a random transformation enhancement (RTE) strategy, which are extensible without introducing extra parameters. All the CASNet components can be combined and learned end-to-end. We further provide a four-stage implementation for evaluation and practical deployments. Experiments demonstrate that CASNet outperforms other CS networks by a large margin, validating the collaboration and mutual supports among its components and strategies. Codes are available at https://github.com/Guaishou74851/CASNet .

中文翻译：

---

内容感知可扩展深度压缩感知

为了更有效地解决图像压缩感知 (CS) 问题，我们提出了一种名为 CASNet 的新型内容感知可扩展网络，它共同实现了自适应采样率分配、细粒度可扩展性和高质量重建。我们首先采用数据驱动的显着性检测器来评估不同图像区域的重要性，并提出一种基于显着性的块比率聚合（BRA）策略来分配采样率。然后开发一个统一的可学习生成矩阵，以生成具有有序结构的任何 CS 比率的采样矩阵。CASNet 配备了由显着性信息引导的优化启发恢复子网和防止块伪影的多块训练方案，CASNet 使用一个模型联合重建以各种采样率采样的图像块。为了加速训练收敛和提高网络鲁棒性，我们提出了一种基于 SVD 的初始化方案和一种随机变换增强 (RTE) 策略，它们可以在不引入额外参数的情况下进行扩展。所有 CASNet 组件都可以端到端组合和学习。我们进一步提供了用于评估和实际部署的四阶段实施。实验表明，CASNet 大大优于其他 CS 网络，验证了其组件和策略之间的协作和相互支持。代码可在 我们进一步提供了用于评估和实际部署的四阶段实施。实验表明，CASNet 大大优于其他 CS 网络，验证了其组件和策略之间的协作和相互支持。代码可在 我们进一步提供了用于评估和实际部署的四阶段实施。实验表明，CASNet 大大优于其他 CS 网络，验证了其组件和策略之间的协作和相互支持。代码可在https://github.com/Guaishou74851/CASNet .