Visual sensor image enhancement based on non-sub-sampled shearlet transform and phase stretch transform

Acquiring clear images is a requisite in visual sensor networks. Image enhancement is an effective way to improve image quality. In this paper, non-sub-sampled shearlet transform (NSST) multi-scale analysis is combined with phase stretch transform (PST) to nonlinearly enhance the images captured by visual sensors. The components of different scales after NSST multi-scale decomposition are processed by nonlinear models with different thresholds. The thresholds of the enhanced model are determined by the local standard deviation of PST feature map. The noise is well suppressed, and the detail features are enhanced obviously. Experiments show that the proposed algorithm can improve image distortion, clear details, and enhance image contrast effectively.