A face super-resolution method based on illumination invariant feature
Kebin Huang, Ruimin Hu, Zhen Han, Tao Lü, Junjun Jiang, Feng Wang · 2011
Human faces in surveillance video images usually have low resolution and poor quality. They need to be reconstructed in super-resolution for identification. The traditional subspace-based face super-resolution algorithms are sensitive to light. For solving the problem, this paper proposes a face super-resolution method based on illumination invariant feature. The method firstly extracts the illumination invariant features of an input low resolution image by using adaptive L1-L2 total variation model and self-quotient image in logarithmic domain. Then it projects the feature onto non-negative basis obtained by Nonnegative Matrix Factorization (NMF) in face image database. Finally it reconstructs the high resolution face images under the framework of Maximum A Posteriori(MAP) probability. Experimental results demonstrate that the proposed method outperforms the compared methods both in subjective and objective quality under poor light conditions.