A MODIFIED PATCH PROPAGATION-BASED IMAGE INPAINTING USING PATCH SPARSITY

We present a modified examplar-based inpainting method in the framework of patch
sparsity. In the examplar-based algorithms, the unknown blocks of target region are inpainted by
the most similar blocks extracted from the source region, using the available information. Defining
a priority term to decide the filling order of missing pixels ensures the connectivity of the object
boundaries. In the exemplar-based patch sparsity approach, a sparse representation of missing
pixels is considered to define a new priority term and the unknown pixels of the fill-front patch is
inpainted by a sparse combination of the most similar patches. Here, we modify this representation
of the priority term and take a measure to compute the similarities between fill-front and candidate
patches. Also, a new definition is proposed for updating the confidence term to illustrate the
amount of the reliable information surrounding pixels. Comparative reconstructed test images show
the effectiveness of our proposed approach in providing high quality inpainted images.