Name
Abstract | ||
|---|---|---|
We present a novel algorithm for reconstructing high-quality defocus blur from a sparsely sampled light field. Our algorithm builds upon recent developments in the area of sheared reconstruction filters and significantly improves reconstruction quality and performance. While previous filtering techniques can be ineffective in regions with complex occlusion, our algorithm handles such scenarios well by partitioning the input samples into depth layers. These depth layers are filtered independently and then combined together, taking into account inter-layer visibility. We also introduce a new separable formulation of sheared reconstruction filters that achieves real-time preformance on a modern GPU and is more than two orders of magnitude faster than previously published techniques. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2699647 | ACM Trans. Graph. |
Keywords | Field | DocType |
algorithms,reconstruction,defocus blur,depth of field,fourier analysis,light field reconstruction | Computer vision,Fourier analysis,Light field,Artificial intelligence,Mathematics,Depth of field | Journal |
Volume | Issue | ISSN |
34 | 2 | 0730-0301 |
Citations | PageRank | References |
3 | 0.38 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karthik Vaidyanathan | 1 | 3 | 1.05 |
| Jacob Munkberg | 2 | 22 | 2.13 |
| Petrik Clarberg | 3 | 247 | 13.64 |
| Marco Salvi | 4 | 98 | 6.80 |