Single depth map super-resolution via a deep feedback network
Guoliang Wu, Yanjie Wang, Li Shi · International Journal of Wavelets Multiresolution and Information Processing · 2020
Existing depth map-based super-resolution (SR) methods cannot achieve satisfactory results in depth map detail restoration. For example, boundaries of the depth map are always difficult to reconstruct effectively from the low-resolution (LR) guided depth map particularly at big magnification factors. In this paper, we present a novel super-resolution method for single depth map by introducing a deep feedback network (DFN), which can effectively enhance the feature representations at depth boundaries that utilize iterative up-sampling and down-sampling operations, building a deep feedback mechanism by projecting high-resolution (HR) representations to low-resolution spatial domain and then back-projecting to high-resolution spatial domain. The deep feedback (DF) block imitates the process of image degradation and reconstruction iteratively. The rich intermediate high-resolution features effectively tackle the problem of depth boundary ambiguity in depth map super-resolution. Extensive experimental results on the benchmark datasets show that our proposed DFN outperforms the state-of-the-art methods.