Gradient-driven PDEs (partial differential equations) are the main computing pattern for geometric inpainting models of digital images.Apparently

compared with previous models

gradient-driven computing models have a great advantage to the large-scale regions geometric inpainting

but its performances are not stable to different inpainted objects because the information propagating direction is uncertain in the inpainting process.Based on analyzing the computing essences and the corresponding physical meanings of gradient-driven models

it is decisive to the inpainting result that the information propagating direction always points to the outside of the inpainted regions.Thus

a new method of gradient-driven image inpainting is proposed.Experimental results prove that the method can stabilize the information propogating direction making its inpainting performance is more robust.