31 research outputs found

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

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    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

    Face hallucination based on nonparametric Bayesian learning

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    © 2015 IEEE. In this paper, we propose a novel example-based face hallucination method through nonparametric Bayesian learning based on the assumption that human faces have similar local pixel structure. We cluster the low resolution (LR) face image patches by nonparametric method distance dependent Chinese Restaurant process (ddCRP) and calculate the centres of the clusters (i.e., subspaces). Then, we learn the mapping coefficients from the LR patches to high resolution (HR) patches in each subspace. Finally, the HR patches of input low resolution face image can be efficiently generated by a simple linear regression. The spatial distance constraint is employed to aid the learning of subspace centers so that every subspace will better reflect the detailed information of image patches. Experimental results show our method is efficient and promising for face hallucination

    Super-resolution:A comprehensive survey

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    Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries

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    Sparse coding-based single image super-resolution has attracted much interest. In this paper, a super-resolution reconstruction algorithm based on sparse coding with multi-class dictionaries is put forward. We propose a novel method for image patch classification, using the phase congruency information. A sub-dictionary is learned from patches in each category. For a given image patch, the sub-dictionary that belongs to the same category is selected adaptively. Since the given patch has similar pattern with the selected sub-dictionary, it can be better represented. Finally, iterative back-projection is used to enforce global reconstruction constraint. Experiments demonstrate that our approach can produce comparable or even better super-resolution reconstruction results with some existing algorithms, in both subjective visual quality and numerical measures

    Facial Texture Super-Resolution by Fitting 3D Face Models

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    This book proposes to solve the low-resolution (LR) facial analysis problem with 3D face super-resolution (FSR). A complete processing chain is presented towards effective 3D FSR in real world. To deal with the extreme challenges of incorporating 3D modeling under the ill-posed LR condition, a novel workflow coupling automatic localization of 2D facial feature points and 3D shape reconstruction is developed, leading to a robust pipeline for pose-invariant hallucination of the 3D facial texture
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