Multi-frequency image completion via a biologically-inspired sub-Riemannian model with frequency and phase
This work addresses image completion for computer vision applications, but it appears incremental as it builds on existing biologically-inspired models without demonstrating broad advancements.
The paper tackled the problem of image completion by introducing a biologically-inspired algorithm that uses a sub-Riemannian model to integrate orientation, frequency, and phase features, achieving completion through diffusion along neural connections, though no concrete performance numbers are provided.
We present a novel cortically-inspired image completion algorithm. It uses a five dimensional sub-Riemannian cortical geometry modelling the orientation, spatial frequency and phase selective behavior of the cells in the visual cortex. The algorithm extracts the orientation, frequency and phase information existing in a given two dimensional corrupted input image via a Gabor transform and represent those values in terms of cortical cell output responses in the model geometry. Then it performs completion via a diffusion concentrated in a neighbourhood along the neural connections within the model geometry. The diffusion models the activity propagation integrating orientation, frequency and phase features along the neural connections. Finally, the algorithm transforms back the diffused and completed output responses back to the two dimensional image plane.