This Paper Introduces A Novel Framework For Example-based Inpainting. It Consists In Performing First The Inpainting On A Coarse Version Of The Input Image. A Hierarchical Super-resolution Algorithm Is Then Used To Recover Details On The Missing Areas. The Advantage Of This Approach Is That It Is Easier To Inpaint Low-resolution Pictures Than High Resolution Ones. The Gain Is Both In Terms Of Computational Complexity And Visual Quality. However, To Be Less Sensitive To The Parameter Setting Of The Inpainting Method, The Low-resolution Input Picture Is In Painted Several Times With Different Configurations. Results Are Efficiently Combined With Loopy Belief Propagation And Details Are Recovered By A Single Image Super-resolution Algorithm. Experimental Results In A Context Of Image Editing And Texture Synthesis Demonstrate The Effectiveness Of The Proposed Method. Results Are Compared To Five State-of-the-art Inpainting Methods.
