Depth Estimation for Lytro Images by Adaptive Window Matching on EPI

• Main Authors: Pei-Hsuan Lin, 林佩璇
• Other Authors: Yung-Yu Chuang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/89166627704402609881

碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 102 === A light field camera, also called a plenoptic camera, is recently becoming accessible in the market. With a micro-lens array locating between the main lens and the sensor, it is able to collect more information than a common camera can do. It is believed that the additional information have the power to open a new era in the field of computation photography. For example, depth of the scene can be estimated, and the depth value can also aid in the applications such as image editing. However, because the development environment is still immature, there exists a bunch of inconvenience for researchers who want to use the camera. Lytro, which we use in this paper, is the cheapest light field camera now in the market, but their producer doesn''t allow users to access the data unless they use the official viewer, not to mention developing other applications. Though there are some toolboxes provided by the third-party to decode the light field pictures, it is still an open problem to obtain such a depth map. We present a method for estimating depth of scenes captured by a Lytro camera. Depth value is computed by adaptive windowing matching on epipolar plane images (EPI) and we achieve data refinement by Markov Random Field (MRF) optimization algorithm, hoping to enhance robustness to noise or other weakness due to hardware limitation. We compare our results with those from existing method for light field depth estimation and show that our method outperforms in most cases. As a result, we believe our work gives researchers or developers hoping to achieve applications such as light field inpainting possibility to break through the limit of existing methods.