Image inpainting using LLE-LDNR and linear subspace mappings

The paper first describes an examplar-based image inpainting algorithm using a locally linear neighbor embedding technique with low-dimensional neighborhood representation (LLE-LDNR). The inpainting algorithm first searches the K nearest neighbors ( ) of the input patch to be filled-in and linearly combine them with LLE-LDNR to synthesize the missing pixels. Linear regression is then introduced for improving the K-NN search. The performance of the LLE-LDNR with the enhanced K-NN search method is assessed for two applications: loss concealment and object removal.