A hybrid sampling and gradient attention network for compressed image sensing

作者：Yang, Xin; Yang, Chunling^*; Chen, Wenjun

来源：VISUAL COMPUTER, 2023, 39(9): 4213-4226.

DOI：10.1007/s00371-022-02585-0

• Deep learning
• Compressed image sensing
• Hybrid sampling
• Block permutation
• Gradient attention

Block compressed sensing (BCS), which is widely used in compressed image sensing (CIS), brings the advantages of lower complexity for sampling and reconstruction. But it will result in redundant information sampling in the smooth block and insufficient information sampling in the texture block, which makes the reconstruction quality of image details poor. To address this problem, we propose a novel Hybrid Sampling and Gradient Attention Network for CIS, dubbed HSGANet. In HSGANet, a new linear sampling strategy, called hybrid BCS (HBCS) sampling is proposed to realize BCS sampling with balanced information entropy for sampling blocks. Specifically, HBCS is composed of the proposed block permutation sampling (BPS) and BCS sampling, where the BPS is used to increase the proportion of texture block information in the image measurement, which is realized by BCS sampling following image block permutation. Furthermore, a selection algorithm is developed to achieve optimal image information balanced permutation. To match HBCS, we design the initial reconstruction fusion sub-network and the deep reconstruction sub-network which is constructed by cascading GA-Blocks with gradient attention sub-network. In each phase, the gradient attention sub-network can achieve pixel-level adaptive fusion of the gradient map obtained by minimizing measurement errors of BPS and BCS. Extensive experimental results show that our HSGANet has a great improvement in reconstruction accuracy than the state-of-the-art methods with a comparable running speed and model complexity.