Context-Patch Face Hallucination Based on Thresholding Locality-constrained Representation and Reproducing Learning

TL;DR

This paper introduces a novel face hallucination method that incorporates contextual patch information and a reproducing learning strategy to improve high-resolution face reconstruction from low-resolution images, especially under misalignment and small sample size issues.

Contribution

It proposes the TLcR-RL framework, combining thresholding-based representation with reproducing learning to enhance accuracy and robustness in face hallucination tasks.

Findings

Significant improvement in hallucination quality both subjectively and objectively.

Enhanced robustness to face misalignment and small sample size problems.

Effective in real-world low-resolution face scenarios.

Abstract

Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL