Depth maps captured with commodity sensors often require super-resolution to be used in applications. In this work we study a super-resolution approach based on a variational problem statement with Tikhonov regularization where the regularizer is parametrized with a deep neural network. This approach was previously applied successfully in photoacoustic tomography. We experimentally show that its application to depth map super-resolution is difficult, and provide suggestions about the reasons for that.

翻译：用商品传感器绘制的深度图往往要求应用时使用超分辨率。 在这项工作中,我们研究了一种基于Tikhonov正规化的变异问题说明的超分辨率方法,其中对正规化器使用深层神经网络进行对称。这种方法以前曾成功地应用于光声波断层摄影。我们实验性地表明,在深度绘图超分辨率上应用该方法是困难的,并提出了有关原因的建议。