Noise robust face hallucination algorithm using local content prior based error shrunk nearest neighbors representation

In recent years face hallucination or super-resolution (SR) is getting much attention due to its wide applicability in real world scenarios. The existing SR methods and models perform well for noise free or small camera/atmospheric noisy faces. However, when suffering from mixed Impulse-Gaussian (MIG) noise, face hallucination becomes a challenging task. To address this problem, a novel error shrunk nearest neighbors representation (ESNNR) based face hallucination algorithm is proposed in this paper. Here, local content prior is incorporated to identify the high variance content (HVC) in the input images. The proposed algorithm suppresses the identified HVC in the input face to minimize the squared error. Moreover, the similarity matching between the input and training images is improved to achieve the locality and sparsity in the presence of MIG noise. Simulation results performed on public FEI, CAS-PEAL, CMU+MIT face databases, and locally captured surveillance video frames show that the proposed algorithm is computationally efficient, suitable for practical applications and give better performance than the existing face SR methods.