Name
Abstract | ||
|---|---|---|
In this work, we propose an algorithm to estimate the depth map of a scene using defocused images. In particular, the depth map is estimated using two defocused images with different depth-of-field for the same scene. Similar to the approach of the general depth from defocus (DFD), the proposed algorithm obtains the depth information from the blurredness of the object. Moreover, our proposed algorithm dramatically improves the accuracy by using both the shallow and deep depth-of-field images, simultaneously. Especially, we propose a novel depth estimation network for dual defocused images using convolutional neural network (CNN). We evaluate our proposed network on the NYU-v2 dataset and show superior performance compared to the existing techniques. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | depth estimation, DFD, dual aperture, neural network |
Field | DocType | ISSN |
Network on,Computer vision,Pattern recognition,Computer science,Convolutional neural network,Artificial intelligence,Depth from defocus,Depth map,Artificial neural network,Depth of field | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gwangmo Song | 1 | 0 | 1.35 |
| Kyoung Mu Lee | 2 | 3228 | 153.84 |