A Face Hallucination Algorithm via an LLE Coefficients Prior Model

In the most of exiting Local linear embedding (LLE)-based image super-resolution methods, a Low resolution (LR) image can be represented as a linear combination of LR training samples. In these methods, the combination coefficients of the LR image are directly used to estimate the High resolution (HR) image. However, experimental results show that the LR-LLE coefficients are different from the corresponding HR-LLE coefficients. To bridge the gap between LR and HR images, a novel LLEbased face hallucination algorithm is proposed. An LLE coefficients prior model is introduced to reduce the coefficient errors. In this prior model, the LLE coefficients of the interpolated LR face image are used to constraint the reconstructed coefficients. Experimental results show that the proposed method can provide improved performance over the compared methods.