How to use Information Theory for Image Inpainting and Blind Spot Filling-in?

This paper shows how information theory can both drive the digital image inpainting process and the optical illusion due to the blind spot. The defended position is that the missing information is padded by the “most probable information around” via a simple filling-in scheme. Thus the proposed algorithm aims to keep the entropy constant. It cares not to create too much novelty as well as not to destroy too much information. For this, the image is broken down into regular squares in order to build a dictionary of unique words and to estimate the entropy. Then the occluded region is completed, word by word and layer by layer, by picking the element which respects the existing image, which minimizes the entropy deviation if there are several candidates, and which limits its potential increase in the case where no compatible word exists and where a new one must be introduced.