This paper introduces a novel deep-learning-based photometric stereo method that uses superresolution (SR) images: SR photometric stereo. Recent deep-learning-based SR algorithms have yielded great results in terms of enlarging images without mosaic effects. Supposing that the SR algorithms successfully enhance the feature and colour information of original images, implementing SR images using the photometric stereo method facilitates the use of considerably more information on the object than existing photometric stereo methods. We built a novel deep-learning-based network for the photometric stereo technique to optimize the input–output of SR image inputs and normal map outputs. We tested our network using the most widely used benchmark dataset and obtained better results than existing photometric stereo methods.

中文翻译：

---

SRPS——使用超分辨率图像的基于深度学习的光度立体

本文介绍了一种新的基于深度学习的光度立体方法，该方法使用超分辨率 (SR) 图像：SR 光度立体。最近基于深度学习的 SR 算法在放大没有马赛克效果的图像方面取得了很好的效果。假设 SR 算法成功地增强了原始图像的特征和颜色信息，使用光度立体方法实现 SR 图像有助于使用比现有光度立体方法更多的对象信息。我们为光度立体技术构建了一个新颖的基于深度学习的网络，以优化 SR 图像输入和法线贴图输出的输入输出。我们使用最广泛使用的基准数据集测试了我们的网络，并获得了比现有光度立体方法更好的结果。