Geometry-Consistent Light Field Super-Resolution via Graph-Based Regularization

Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus to depth estimation and image-based rendering. However, light field cameras suffer by design from s...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 27(2018), 9 vom: 01. Sept., Seite 4207-4218
1. Verfasser: Rossi, Mattia (VerfasserIn)
Weitere Verfasser: Frossard, Pascal
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2018
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
LEADER 01000caa a22002652 4500
001 NLM285152971
003 DE-627
005 20250223154230.0
007 cr uuu---uuuuu
008 231225s2018 xx |||||o 00| ||eng c
024 7 |a 10.1109/TIP.2018.2828983  |2 doi 
028 5 2 |a pubmed25n0950.xml 
035 |a (DE-627)NLM285152971 
035 |a (NLM)29870342 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Rossi, Mattia  |e verfasserin  |4 aut 
245 1 0 |a Geometry-Consistent Light Field Super-Resolution via Graph-Based Regularization 
264 1 |c 2018 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 30.07.2018 
500 |a Date Revised 30.07.2018 
500 |a published: Print 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus to depth estimation and image-based rendering. However, light field cameras suffer by design from strong limitations in their spatial resolution. Off-the-shelf super-resolution algorithms are not ideal for light field data, as they do not consider its structure. On the other hand, the few super-resolution algorithms explicitly tailored for light field data exhibit significant limitations, such as the need to carry out a costly disparity estimation procedure with sub-pixel precision. We propose a new light field super-resolution algorithm meant to address these limitations. We use the complementary information in the different light field views to augment the spatial resolution of the whole light field at once. In particular, we show that coupling the multi-view approach with a graph-based regularizer, which enforces the light field geometric structure, permits to avoid the need of a precise and costly disparity estimation step. Extensive experiments show that the new algorithm compares favorably to the state-of-the-art methods for light field super-resolution, both in terms of visual quality and in terms of reconstruction error 
650 4 |a Journal Article 
700 1 |a Frossard, Pascal  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society  |d 1992  |g 27(2018), 9 vom: 01. Sept., Seite 4207-4218  |w (DE-627)NLM09821456X  |x 1941-0042  |7 nnns 
773 1 8 |g volume:27  |g year:2018  |g number:9  |g day:01  |g month:09  |g pages:4207-4218 
856 4 0 |u http://dx.doi.org/10.1109/TIP.2018.2828983  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 27  |j 2018  |e 9  |b 01  |c 09  |h 4207-4218