In this paper, we propose a new method to super-resolve low resolution human body images by learning efficient multi-scale features and exploiting useful human body prior. Specifically, we propose a lightweight multi-scale block (LMSB) as basic module of a coherent framework, which contains an image reconstruction branch and a prior estimation branch. In the image reconstruction branch, the LMSB aggregates features of multiple receptive fields so as to gather rich context information for low-to-high resolution mapping. In the prior estimation branch, we adopt the human parsing maps and nonsubsampled shearlet transform (NSST) sub-bands to represent the human body prior, which is expected to enhance the details of reconstructed human body images. When evaluated on the newly collected HumanSR dataset, our method outperforms state-of-the-art image super-resolution methods with $\sim 8\times $ fewer parameters; moreover, our method significantly improves the performance of human image analysis tasks (e.g. human parsing and pose estimation) for low-resolution inputs.

中文翻译：

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一种精确，轻量的人体图像超分辨率方法

在本文中，我们提出了一种通过学习有效的多尺度特征并事先利用有用的人体来超分辨低分辨率人体图像的新方法。具体来说，我们提出了一个轻量级的多尺度块（LMSB）作为相干框架的基本模块，该模块包含一个图像重建分支和一个先验估计分支。在图像重建分支中，LMSB汇总了多个接收场的特征，以便收集丰富的上下文信息以进行从低到高分辨率的映射。在先验估计分支中，我们采用人体解析图和非下采样的小波变换（NSST）子带来表示人体先验，这有望增强重建的人体图像的细节。在新收集的HumanSR数据集上进行评估时， $ \ sim 8 \ times $ 参数较少；此外，对于低分辨率输入，我们的方法显着提高了人类图像分析任务（例如，人类解析和姿势估计）的性能。