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。

内容感知可扩展深度压缩感知

Content-aware Scalable Deep Compressed Sensing

Recent progress on salient object detection mainly aims at exploiting how to
effectively integrate multi-scale convolutional features in convolutional
neural networks (CNNs). Many popular methods impose deep supervision to perform
side-output predictions that are linearly aggregated for final saliency
prediction. In this paper, we theoretically and experimentally demonstrate that
linear aggregation of side-output predictions is suboptimal, and it only makes
limited use of the side-output information obtained by deep supervision. To
solve this problem, we propose Deeply-supervised Nonlinear Aggregation (DNA)
for better leveraging the complementary information of various side-outputs.
Compared with existing methods, it i) aggregates side-output features rather
than predictions, and ii) adopts nonlinear instead of linear transformations.
Experiments demonstrate that DNA can successfully break through the bottleneck
of current linear approaches. Specifically, the proposed saliency detector, a
modified U-Net architecture with DNA, performs favorably against
state-of-the-art methods on various datasets and evaluation metrics without
bells and whistles.

本文提出了一种名为 DNA 的深度监督非线性聚合方法，以更好地利用深度监督得到的各种侧输出的互补信息，进而提高显著性检测的效果。该方法采用非线性聚合来代替传统线性聚合方法，并在各种数据集和评价指标上实现了很好的效果。