Name
Abstract | ||
|---|---|---|
Blurring depth edges and texture copy artifacts are challenging issues for guided depth map upsampling. They are caused by the inconsistency between depth edges and corresponding color edges. In this paper, we extend the well-known Joint Bilateral Upsampling (JBU) (Kopf et al. 2007) with a novel non-convex optimization framework for guided depth map upsampling, which is denoted as Non-Convex JBU (NCJBU). We show that the proposed NCJBU can well handle the edge inconsistency by making use of the property of both the guidance color image and the depth map. Through comprehensive experiments, we show that our NCJBU can preserve sharp depth edges and properly suppress texture copy artifacts. In addition, we present a data driven scheme to properly determine the parameter in our model such that fine details and sharp depth edges are well preserved even for a large upsampling factor (e.g., 8 ×). Experimental results on both simulated and real data show the effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11042-017-5131-x | Multimedia Tools Appl. |
Keywords | Field | DocType |
Non-convex joint bilateral upsampling, Guided depth map upsampling | Computer vision,Data-driven,Pattern recognition,Computer science,Regular polygon,Artificial intelligence,Depth map,Upsampling,Color image | Journal |
Volume | Issue | ISSN |
77 | 12 | 1380-7501 |
Citations | PageRank | References |
3 | 0.37 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiankai Lu | 1 | 65 | 9.78 |
| Yiyou Guo | 2 | 4 | 2.75 |
| na liu | 3 | 7 | 2.10 |
| Lihong Wan | 4 | 12 | 3.54 |
| Tao Fang | 5 | 226 | 31.10 |