Image inpainting with Markov chains

This paper presents a novel context-based image inpainting method. The proposed technique is applying Markov chains to restore the colors of objects from images affected by some external factors (like scratches or wipes) or partially covered by other objects. Thus, damages or unwanted objects can be removed from an image by replacing each pixel from such an area, based on the surrounding unaffected context information. Therefore, the restoration process is applied from the exterior to the interior, by using for replacement colors occurring with the highest probability in similar contexts. Since we use context information, the proposed inpainting technique can successfully rebuild details in images. We have compared our method with other existing inpainting techniques, and the results were better on some test images or comparable on others.

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