Cortical-inspired image reconstruction via sub-Riemannian geometry and hypoelliptic diffusion
This work addresses image reconstruction for computer vision applications, but it is incremental as it reviews and compares existing algorithms rather than introducing new ones.
The paper tackles image inpainting by reviewing algorithms based on hypoelliptic diffusion from a model of the primary visual cortex, showing that algorithms using information about corrupted areas achieve state-of-the-art reconstructions, while those without are limited to recognizable images.
In this paper we review several algorithms for image inpainting based on the hypoelliptic diffusion naturally associated with a mathematical model of the primary visual cortex. In particular, we present one algorithm that does not exploit the information of where the image is corrupted, and others that do it. While the first algorithm is able to reconstruct only images that our visual system is still capable of recognize, we show that those of the second type completely transcend such limitation providing reconstructions at the state-of-the-art in image inpainting. This can be interpreted as a validation of the fact that our visual cortex actually encodes the first type of algorithm.