结合图像信号显著性的自适应分块压缩采样

均匀分块压缩感知对图像信号进行压缩采样, 无法有效地分离出重要区域和背景区域。为此，本文提出了一种基于显著性的自适应分块压缩采样方法。根据图像信号的显著性，该方法利用四叉树算法进行自适应图像分块，有效分离出重要区域和背景区域。根据区域块的显著度动态设置观测值数量，重要度区域设置高采样率，背景区域设置低采样率，从而提高重要区域的图像重建质量。实验分析表明，在得到更好的视觉效果的同时，本文算法观测值数量较少，且重构图像的PSNR(峰值信噪比)、MSSIM（平均结构相似性）指标，以及运行时间均优于均匀分块压缩采样算法。

Saliency-based adaptive block compressive sampling for image signals

Uniform block compressed sensing cannot separate the important region from the background for image signals effectively. A new notion of saliency-based adaptive block compressive sampling method is proposed. According to the saliency of the image signal, the quadtree algorithm is introduced to separate the important block and background block adaptively. The amount of observation samples is assigned dynamically to improve the quality of image reconstruction in salient regions, the high sampling rate is set for the important regions, while the low value for the background regions. Experimental results validate its rationality and effectiveness. Compared with uniform block compressed sensing, the proposed method needs fewer observations, and have the better performance in PSNR, MSSIM with shorter running time.