Super-resolution reconstruction of faces by enhanced global models of shape and texture

We present a computationally efficient method for the super-resolution reconstruction of face images from their low-resolution versions. It is based on generative models and utilizes both the shape and texture components together. The main idea is that the image details can be synthesized by global modeling of accurately aligned local image regions. In order to achieve sufficient accuracy in alignment, shape reconstruction is considered as a separate problem and solved together with texture reconstruction in a coordinated manner. Meanwhile, the statistical dependency between the shape and texture components is also considered. Moreover, different from traditional model-based super-resolution methods, we use a corrected form of the degradation operator with the aligned images. We show that when the degradation is used with the aligned texture components as is, it causes bias in the reconstructions. To overcome this problem, we reflect the same processing performed in alignment onto the degradation operator and use this corrected version in texture reconstruction. Experimental results show that the proposed solution provides superior image reconstructions (both qualitatively and quantitatively) in a faster way.

► Face image reconstruction is approached by global models of shape and texture.
► Shape reconstruction is handled separately and solved with texture coordinately.
► The dependency of image components is employed.
► Stability problems, caused by the utilization of shape, are considered.
► Reconstruction complexity is reduced; from quadratic time to logarithmic time.