Blind Facial Image Quality Enhancement Using Non-Rigid Semantic Patches

We propose a new way to solve a very general blind inverse problem of multiple simultaneous degradations, such as blur, resolution reduction, noise, and contrast changes, without explicitly estimating the degradation. The proposed concept is based on combining semantic non-rigid patches, problem-specific high-quality prior data, and non-rigid registration tools. We show how a significant quality enhancement can be achieved, both visually and quantitatively, in the case of facial images. The method is demonstrated on the problem of cellular photography quality enhancement of dark facial images for different identities, expressions, and poses, and is compared with the state-of-the-art denoising, deblurring, super-resolution, and color-correction methods.