Zhou Dongsheng, Wang Ruyi, Yang Xin, Zhang Qiang, Wei Xiaopeng
Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian 116622, People's Republic of China.
College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, People's Republic of China.
R Soc Open Sci. 2019 Jan 30;6(1):181074. doi: 10.1098/rsos.181074. eCollection 2019 Jan.
Depth image super-resolution (SR) is a technique that uses signal processing technology to enhance the resolution of a low-resolution (LR) depth image. Generally, external database or high-resolution (HR) images are needed to acquire prior information for SR reconstruction. To overcome the limitations, a depth image SR method without reference to any external images is proposed. In this paper, a high-quality edge map is first constructed using a sparse coding method, which uses a dictionary learned from the original images at different scales. Then, the high-quality edge map is used to guide the interpolation for depth images by a modified joint trilateral filter. During the interpolation, some information of gradient and structural similarity (SSIM) are added to preserve the detailed information and suppress the noise. The proposed method can not only preserve the sharpness of image edge, but also avoid the dependence on database. Experimental results show that the proposed method is superior to some state-of-the-art depth image SR methods.
深度图像超分辨率（SR）是一种利用信号处理技术来提高低分辨率（LR）深度图像分辨率的技术。一般来说，需要外部数据库或高分辨率（HR）图像来获取用于SR重建的先验信息。为了克服这些限制，提出了一种无需参考任何外部图像的深度图像SR方法。在本文中，首先使用稀疏编码方法构建高质量边缘图，该方法使用从不同尺度的原始图像中学习到的字典。然后，通过改进的联合双边滤波器，利用高质量边缘图来指导深度图像的插值。在插值过程中，添加了一些梯度和结构相似性（SSIM）信息，以保留详细信息并抑制噪声。所提出的方法不仅可以保留图像边缘的清晰度，还可以避免对数据库的依赖。实验结果表明，该方法优于一些现有的深度图像SR方法。
