Image imputation based on clustering similarity comparison

This paper presents a method to fill in missing data in an image. If the missing data are clustered in forms of an empty shape, then a similarity pattern searching and filling is performed. The missing data areas are divided into a set of windows of equal size. Each windowed area will be compared with every other non-missing data area of the original image to find the area that is most similar to the missing area. The experimental results show that in several cases our proposed algorithms outperform traditional methods.