Alternately Guided Depth Super-resolution Using Weighted Least Squares and Zero-order Reverse Filtering

Abstract

Due to the structural inconsistency between color and depth, texture copying and edge blurring artifacts appear in color-guided depth super-resolution. In this paper, we propose alternately guided depth super-resolution using weighted least squares (WLS) and zero-order reverse filtering. We adopt WLS for alternating guidance, and alternately use color and depth as guidance in WLS to suppress texture copying artifacts. Since color guidance causes edge blurs in depth due to the mismatch between color and depth, we apply zero-order reverse filtering to depth images to alleviate edge blurring artifacts. Moreover, WLS is a global optimization-based filter and thus it is effective in removing depth noise. Experiments on Middlebury and real scene datasets show that the proposed method outperforms state-of-the-art methods in terms of quantitative and qualitative measurements.