Existing image compressed sensing (CS) coding frameworks usually solve an inverse problem based on measurement coding and optimization-based image reconstruction, which still exist the following two challenges: 1) The widely used random sampling matrix, such as the Gaussian Random Matrix (GRM), usually leads to low measurement coding efficiency. 2) The optimization-based reconstruction methods generally maintain a much higher computational complexity. In this paper, we propose a new CNN based image CS coding framework using local structural sampling (dubbed CSCNet) that includes three functional modules: local structural sampling, measurement coding and Laplacian pyramid reconstruction. In the proposed framework, instead of GRM, a new local structural sampling matrix is first developed, which is able to enhance the correlation between the measurements through a local perceptual sampling strategy. Besides, the designed local structural sampling matrix can be jointly optimized with the other functional modules during training process. After sampling, the measurements with high correlations are produced, which are then coded into final bitstreams by the third-party image codec. At last, a Laplacian pyramid reconstruction network is proposed to efficiently recover the target image from the measurement domain to the image domain. Extensive experimental results demonstrate that the proposed scheme outperforms the existing state-of-the-art CS coding methods, while maintaining fast computational speed.

翻译：现有图像压缩感知（CS）编码框架通常基于测量编码和优化驱动的图像重建来解决逆问题，但仍存在以下两个挑战：1）广泛使用的随机采样矩阵（如高斯随机矩阵）通常导致测量编码效率低下；2）基于优化的重建方法通常计算复杂度较高。本文提出一种基于局部结构采样的新型CNN图像CS编码框架（命名为CSCNet），该框架包含三个功能模块：局部结构采样、测量编码和拉普拉斯金字塔重建。在所提框架中，首先摒弃GRM，开发了一种新型局部结构采样矩阵，通过局部感知采样策略增强测量值之间的相关性。此外，所设计的局部结构采样矩阵可在训练过程中与其他功能模块联合优化。采样后生成具有高相关性的测量值，随后通过第三方图像编解码器编码为最终比特流。最后，提出拉普拉斯金字塔重建网络，将目标图像从测量域高效重建至图像域。大量实验结果表明，所提方案在保持快速计算速度的同时，优于现有最先进的CS编码方法。