  We propose to combine semantic data and registration algorithms to solve
various image processing problems such as denoising, super-resolution and
color-correction. It is shown how such new techniques can achieve significant
quality enhancement, both visually and quantitatively, in the case of facial
image enhancement. Our model assumes prior high quality data of the person to
be processed, but no knowledge of the degradation model. We try to overcome the
classical processing limits by using semantically-aware patches, with adaptive
size and location regions of coherent structure and context, as building
blocks. The method is demonstrated on the problem of cellular photography
enhancement of dark facial images for different identities, expressions and
poses.
