  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.
