Time-of-flight (ToF) cameras calculate depth maps by reconstructing phase
shifts of amplitude-modulated signals. For broad illumination or transparent
objects, reflections from multiple scene points can illuminate a given pixel,
giving rise to an erroneous depth map. We report here a sparsity regularized
solution that separates K-interfering components using multiple modulation
frequency measurements. The method maps ToF imaging to the general framework of
spectral estimation theory and has applications in improving depth profiles and
exploiting multiple scattering.

使用多个调制频率测量分离干涉成分的稀疏正则化解决方案，将 ToF 成像映射到频谱估计理论的一般框架中，并应用于改善深度剖面和利用多重散射。

利用调制频率多样性和稀疏正则化解决飞行时间成像中的多径干扰

Resolving Multi-path Interference in Time-of-Flight Imaging via  Modulation Frequency Diversity and Sparse Regularization

High-resolution depth maps can be inferred from low-resolution depth
measurements and an additional high-resolution intensity image of the same
scene. To that end, we introduce a bimodal co-sparse analysis model, which is
able to capture the interdependency of registered intensity and depth
information. This model is based on the assumption that the co-supports of
corresponding bimodal image structures are aligned when computed by a suitable
pair of analysis operators. No analytic form of such operators exist and we
propose a method for learning them from a set of registered training signals.
This learning process is done offline and returns a bimodal analysis operator
that is universally applicable to natural scenes. We use this to exploit the
bimodal co-sparse analysis model as a prior for solving inverse problems, which
leads to an efficient algorithm for depth map super-resolution.

通过学习联合分析运算符的方法，提出了一种双模共稀疏分析模型，将强度图像和深度信息结合起来用于高分辨率深度图像的超分辨率重建。