Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks
| Autor: | M. Shahzeb Khan Gul, Bahadir K. Gunturk |
|---|---|
| Přispěvatelé: | Gul, M. Shahzeb Khan, Gunturk, Bahadir K. Istanbul Medipol Univ, Dept Elect & Elect Engn, TR-34810 Istanbul, Turkey, Gunturk, Bahadir -- 0000-0003-0779-9620 |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Aperture Computer science Computer Vision and Pattern Recognition (cs.CV) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition convolutional neural network super-resolution 02 engineering and technology Convolutional neural network law.invention Optics law 0202 electrical engineering electronic engineering information engineering Angular resolution Image resolution Light field Light-field camera business.industry Photography 020206 networking & telecommunications Computer Graphics and Computer-Aided Design 020201 artificial intelligence & image processing business Software |
| Popis: | WOS: 000426272000006 PubMed ID: 29432097 Light field imaging extends the traditional photography by capturing both spatial and angular distribution of light, which enables new capabilities, including post-capture refocusing, post-capture aperture control, and depth estimation from a single shot. Micro-lens array (MLA) based light field cameras offer a cost-effective approach to capture light field. A major drawback of MLA based light field cameras is low spatial resolution, which is due to the fact that a single image sensor is shared to capture both spatial and angular information. In this paper, we present a learning based light field enhancement approach. Both spatial and angular resolution of captured light field is enhanced using convolutional neural networks. The proposed method is tested with real light field data captured with a Lytro light field camera, clearly demonstrating spatial and angular resolution improvement. TUBITAK [114E095] This work was supported by TUBITAK under Grant 114E095. |
| Databáze: | OpenAIRE |
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