Highly corrupted image inpainting through hypoelliptic diffusion
This addresses image inpainting for highly corrupted images, which is an incremental improvement in a domain-specific area.
The paper tackles the problem of reconstructing highly corrupted images with over 80% missing data using the Averaging and Hypoelliptic Evolution (AHE) algorithm, achieving results comparable to state-of-the-art methods.
We present a new image inpainting algorithm, the Averaging and Hypoelliptic Evolution (AHE) algorithm, inspired by the one presented in [SIAM J. Imaging Sci., vol. 7, no. 2, pp. 669--695, 2014] and based upon a semi-discrete variation of the Citti-Petitot-Sarti model of the primary visual cortex V1. The AHE algorithm is based on a suitable combination of sub-Riemannian hypoelliptic diffusion and ad-hoc local averaging techniques. In particular, we focus on reconstructing highly corrupted images (i.e. where more than the 80% of the image is missing), for which we obtain reconstructions comparable with the state-of-the-art.