Intensity-Guided Depth Upsampling Using Edge Sparsity and Weighted $L_{0}$ Gradient Minimization
Shengtao Yu, Cheolkon Jung, Inyong Yun, Joongkyu Kim · 2018
Although depth cameras acquire depth in dynamic scenes, the captured depth images are often noisy and of low resolution. In this paper, we propose intensity-guided depth upsampling using edge sparsity and weighted L0 gradient minimization. We exploit the edge sparsity in the gradient domain to enhance the resolution and quality of depth images. First, we get mutual structure between intensity and depth using joint mutual-structure filtering. Second, we generate weights for L0 gradient minimization based on gradient and entropy of the intensity image, and upsample the depth image using weighted L0 gradient minimization. Finally, we refine the depth image using adaptive fast weighted median filtering. Experiments on Middlebury 2005 and real-world scene datasets confirm that the proposed method produces edge-preserving depth upsampling results and outperforms state-of-the-art methods in terms of accuracy.