Adaptive missing texture reconstruction method based on kernel cross-modal factor analysis with a new evaluation criterion

• An adaptive missing texture reconstruction method is newly proposed.
• Relationship between unknown and known areas is modeled by kernel cross-modal factor analysis.
• A new criterion is adopted to adaptively select training data optimal for target missing areas.
• Patch priority estimation is also realized based on the above new criterion.
• Performance improvement is verified in subjective and quantitative evaluation.

This paper presents an adaptive missing texture reconstruction method based on kernel cross-modal factor analysis (KCFA) with a new evaluation criterion. The proposed method estimates the latent relationship between two areas, which correspond to a missing area and its neighboring area, respectively, from known parts within the target image and realizes reconstruction of the missing textures. In order to obtain this relationship, KCFA is applied to each cluster containing similar known textures, and the optimal cluster is used for reconstructing each target missing area. Specifically, a new criterion obtained by monitoring errors caused in the latent space enables selection of the optimal cluster. Then each missing texture is adaptively estimated by the optimal cluster's latent relationship, which enables accurate reconstruction of similar textures. In our method, the above criterion is also used for estimating patch priority, which determines the reconstruction order of missing areas within the target image. Since patches, whose textures are accurately modeled by our KCFA-based method, can be selected by using the new criterion, it becomes feasible to perform successful reconstruction of the missing areas. Experimental results show improvements of our KCFA-based reconstruction method over previously reported methods.