Image Inpainting Based On Partial Differential Equation (PDE)

Inpainting is to fill in image information on a blank domain based upon the image information available outside, it is a inter polation problem. It has wide applications in restoring scratched old photos, disocclusions in vision analysis, text removal from images, and digital zoomings. Image inpainting is a new area of image restoration. Recently, it is regarded by many researchers in Image Processing area at home and abroad.PDE-based digital image processing is a new research topic. Because of its efficiency in experiement. more and more mathematicians begin to notice it. Now. PDEs have been widely used in many aspects of image processing and computer visual,including image aeomentation-tracking moving objects, object edges detection, image interpolation and so on; which have been obtained many expected results.We studied several inpainting methods especially, including the Bertalmio-Sapiro-Caselles-Ballester (BSCB) model and Curvature Driven Diffusions (CDD) model based on PDE, and total variation (TV) model based on the Bayesian and variational principles, also provided different inpainting effect of each model.This paper puts emphasis on comparing CDD model with fast inpainting model based on the Curvature Driven Diffusions .They all realize the connectivity principle,and have good ability for inpainting the large demain and minute edges, but the former inpainting speed is quite slow.,the latter can overcome CDD model's shortcoming of slow speed. Experiment demonstrates this fast inpainting model can greatly advance the speed in inpainting , moreover, be provided with the same quality off CDD model.