Lightweight Guidance Sampling and Deep Refinement Reconstruction Network for Adaptive Compressive Sensing

Abstract

Adaptive Compressive Sensing (ACS) has attracted increasing attention for its ability to progressively improve image reconstruction quality by dynamically adjusting sampling allocation. Multi-stage sampling is a promising strategy that leverages intermediate reconstructions to guide sampling without relying on image prior information. However, existing multi-stage methods often struggle to capture global structural information, resulting in biased sampling and suboptimal performance. Furthermore, the strong dependency between intermediate reconstruction for sampling guidance and the final reconstruction can hinder targeted optimization. To address these issues, we propose LGDR-Net, a Lightweight Guidance Sampling and Deep Refinement Reconstruction Network. Specifically, the Gradient-Fused Cross-Attention (GFCA) module, embedded within a lightweight guidance network, leverages globally fused information to compensate for incomplete content during multi-stage sampling. Then, sampling resource allocation is driven by inter-stage reconstruction differences, effectively exploiting image sparsity information. Finally, the Deep Refinement Network incorporates a Decoder Dense Feedback Mechanism (DDFM) to reduce cross-layer structural bias and a Multi-Branch Attention Fusion (MBAF) module for improved fine-texture representation. Extensive experiments demonstrate that our proposed LGDR-Net outperforms state-of-the-art methods, achieving an excellent trade-off between computational cost and reconstruction quality.