Color-guided boundary-preserving depth upsampling based on L0 gradient minimization
Shengtao Yu, Cheolkon Jung, Joongkyu Kim · 2016
Although a time-of-flight (ToF) depth camera provides depth information in dynamic scenes, captured depth images are often noisy and of low resolution. In this paper, we propose color-guided boundary-preserving depth upsampling based on L0gradient minimization to enhance both resolution and visual quality of depth images. Guided by the high-resolution color image, we perform optimization to preserve boundaries along objects. We apply the L0gradient to the regularization term, and compute its weights from both color and depth images. We optimize the objective function using alternative minimization and half-quadratic splitting. Experimental results on Middlebury 2005 and real-world scene datasets demonstrate that the proposed method produces boundary-preserving depth upsampling results and outperforms state-of-the-art ones in terms of accuracy.