2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | TEC-4.12 | ||
| Paper Title | DEPTH MAP SUPER-RESOLUTION BY MULTI-DIRECTION DICTIONARY AND JOINT REGULARIZATION | ||
| Authors | Wei Xu, Jin Wang, Longhua Sun, Qing Zhu, Faculty of Information Technology, China | ||
| Session | TEC-4: Super-resolution | ||
| Location | Area G | ||
| Session Time: | Wednesday, 22 September, 14:30 - 16:00 | ||
| Presentation Time: | Wednesday, 22 September, 14:30 - 16:00 | ||
| Presentation | Poster | ||
| Topic | Image and Video Processing: Interpolation, super-resolution, and mosaicing | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Depth maps acquired by 3D cameras usually suffer from low resolution and insufficient quality, which makes it difficult to be directly used in visual depth perception and 3D reconstruction. To handle this problem, we propose a novel multi-direction dictionary and joint regularization model for high quality depth recovery. To enhance the sparsity, image is divided into classified patches according to the same geometrical direction and a compact dictionary is trained within each class. Then for a patch to be coded, the most relevant dictionary can be selected according to its geometrical direction. We further introduce two regularization terms into the reconstruction model. One is the anisotropic total variation (TV) defined by the local gradients of color/depth pair. The other is the nonlocal similarity to provide nonlocal constraint to the local structure. Experimental results demonstrate that our method outperforms other state-of-the-art methods in terms of both subjective quality and objective quality. | ||