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Sparse representation and position prior based face hallucination upon classified over-complete dictionaries

机译：分类过的完整字典的稀疏表示和基于位置先验的幻觉

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摘要

In compressed sensing theory, decomposing a signal based upon redundant dictionaries is of considerable interest for data representation in signal processing. The signal is approximated by an over-complete dictionary instead of an orthonormal basis for adaptive sparse image decompositions. Existing sparsity-based super-resolution methods commonly train all atoms to construct only a single dictionary for super-resolution. However, this approach results in low precision of reconstruction. Furthermore, the process of generating such dictionary usually involves a huge computational cost. This paper proposes a sparse representation and position prior based face hallucination method for single face image super-resolution. The high- and low-resolution atoms for the first time are classified to form local dictionaries according to the different regions of human face, instead of generating a single global dictionary. Different local dictionaries are used to hallucinate the corresponding regions of face. The patches of the low-resolution face inputs are approximated respectively by a sparse linear combination of the atoms in the corresponding over-complete dictionaries. The sparse coefficients are then obtained to generate high-resolution data under the constraint of the position prior of face. Experimental results illustrate that the proposed method can hallucinate face images of higher quality with a lower computational cost compared to other existing methods.
机译：在压缩感测理论中，基于冗余字典分解信号对于信号处理中的数据表示非常重要。该信号通过一个过完整的字典而不是用于自适应稀疏图像分解的正交基础来近似。现有的基于稀疏性的超分辨率方法通常训练所有原子以仅构建用于超分辨率的单个字典。但是，这种方法导致重建精度低。此外，生成这样的字典的过程通常涉及巨大的计算成本。提出了一种基于稀疏表示和位置先验的人脸幻觉方法，用于单人脸图像超分辨率。首次将高分辨率和低分辨率的原子分类以根据人脸的不同区域形成局部词典，而不是生成单个全局词典。使用不同的局部词典来幻化面部的相应区域。低分辨率人脸输入的色块分别通过相应超完备字典中原子的稀疏线性组合来近似。然后获得稀疏系数以在面部先于位置的约束下生成高分辨率数据。实验结果表明，与其他现有方法相比，所提出的方法可以以较低的计算量产生更高质量的人脸图像幻觉。

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