Super resolution face image based on locally linear embedding and local correlation

Abstract

In this paper, we propose a new face hallucination algorithm based on Locally Linear Embedding and Local Correlation method (LC-LLE). The LC-LLE algorithm is an improved locally linear embedding (LLE) algorithm by combining LLE algorithm and local correlation coefficients. The main difference between LC-LLE and LLE algorithms is that LC-LLE uses two different measures for searching the nearest neighbors for matching the most similar patches, while LLE uses only Euclidean distance for searching the nearest neighbors. Specifically, we calculate the Euclidean distance between the low-resolution input patch and patches in the low-resolution training images to select z -NN (i.e. z number of nearest patches). Then, we use the inner product for local correlation computation between the input patch and selected z -NN to identify k nearest neighbors (i.e. k -NN). After that the reconstruction weights are derived using k -NN patches, and generate the high-resolution image patches based on the reconstruction weights. Finally, high-resolution patches are synthesized into the high-resolution image. Experimental results show that the proposed method achieves better performance for high-resolution image reconstruction than Ma's method with LLE and PCA methods.