2D semi-supervised CCA-based inpainting including new priority estimation

This paper presents an inpainting method based on 2D semi-supervised canonical correlation analysis (2D semi-CCA) including new priority estimation. The proposed method estimates relationship, i.e., the optimal correlation, between missing area and its neighboring area from known parts within the target image by using 2D CCA. In this approach, we newly introduce a semi-supervised scheme into the 2D CCA for deriving the 2D semi-CCA which corresponds to a hybrid version of 2D CCA and 2D principle component analysis (2D PCA). This enables successful relationship estimation even if sufficient number of training pairs cannot be provided. Then, by using the obtained relationship, accurate estimation of the missing intensities can be realized. Furthermore, in the proposed method, errors caused in the new variate space obtained by the 2D semi-CCA are effectively used for deriving patch priority determining inpainting order of missing areas. Experimental results show our inpainting method can outperform previously reported methods.