Scalable compressive sampling network with progressive hierarchical subspace learning

Zhu Yin, Zhongcheng Wu, Wuzhen Shi

Abstract: Highlights•We propose a progressive hierarchical sampling strategy based on a three-level wavelet transform, which realizes constructing a lightweight compressive sampling matrix, and enables frequency band-separated sampling through band-aware masks.•We present the independent sampling of specific frequency bands strategy, which allows us to focus on the acquisition of important frequency bands at low sampling ratios, and the restoration of image details as the sampling ratio increases.•Extensive experiments demonstrate the effectiveness of our approach, and it is well-suited for deployment terminal devices in resource-limited scenarios.