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 importances 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
this https URL

提出了一种新的内容感知可扩展网络 CASNet，采用数据驱动的显着性检测器评估不同图像区域的重要性，并提出基于显着性的块比率聚合策略进行采样率分配，用联合可学习的生成矩阵生成任何 CS 比率的采样矩阵。同时，CASNet 拥有优化启发式恢复子网络和防止阻塞伪影的多块训练方案，该网络能够联合重建不同采样率采样的图像块。