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Pixel-wise dictionary learning based locality-constrained representation for noise robust face hallucination

机译：基于噪声鲁棒面幻觉的基于像素 - 方面的词典学习基于地区限制表示

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摘要

Recently, the sparsity and locality constrained linear coding (SLLC) attracted much attention in image super-resolution (SR) applications. However, the current SLLC based SR methods are too weak to handle the impulse noise problem. Therefore, this work presents a dictionary learning-based locality-constrained representation (DLcR) for robust face hallucination. It simultaneously hallucinates the face images and suppresses the noise (outliers) using the noisy pixel information in the observed faces. It first identifies the correct position of the noisy pixels in the input face image and then learns the position-wise low-resolution (LR) dictionary images with the detected noise. This learning makes the similar noisy structure of LR dictionary images as the input LR face, which minimizes the error in optimal weight reconstruction. In addition, the performance of DLcR is improved further by imposing the appropriate thresholds on the LR dictionary, named T-DLcR. The thresholding of the LR dictionary in T-DLcR leads to represent the input LR patch through its nearest LR dictionary patches with the precise reconstruction weights. The comparison results of T-DLcR with several position-patch based face SR methods and recent deep learning-based SR method show its superiority on standard as well as real-world face datasets. (C) 2020 Elsevier Inc. All rights reserved.
机译：最近，稀疏性和局部性约束线性编码（SLLC）在图像超分辨率（SR）应用中引起了很多关注。但是，基于SLLC的SR方法太弱而无法处理脉冲噪声问题。因此，这项工作介绍了基于词典的基于词典的地区限制表示（DLCR），用于鲁棒面幻觉。它同时使用观察到的面部中的噪声像素信息来暂时幻觉并抑制噪声（异常值）。它首先识别输入面部图像中的噪声像素的正确位置，然后使用检测到的噪声来学习位置明智的低分辨率（LR）字典图像。该学习使LR字典图像的类似噪声结构作为输入LR面部，这最小化了最佳重量重构中的误差。此外，通过强加于LR字典的适当阈值，进一步提高了DLCR的性能，命名为T-DLCR。 T-DLCR中LR字典的阈值平衡通过其最近的LR字典块与精确的重建权重表示输入LR贴片。 T-DLCR与若干位置贴片基面SR方法的比较结果与最近的基于深度学习的SR方法显示其对标准的优势以及真实世界的面部数据集。 （c）2020 Elsevier Inc.保留所有权利。

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