Learning to capture light fields through a coded aperture camera

概要

We propose a learning-based framework for acquiring a light field through a coded aperture camera. Acquiring a light field is a challenging task due to the amount of data. To make the acquisition process efficient, coded aperture cameras were successfully adopted; using these cameras, a light field is computationally reconstructed from several images that are acquirToshiakied with different aperture patterns. However, it is still difficult to reconstruct a high-quality light field from only a few acquired images. To tackle this limitation, we formulated the entire pipeline of light field acquisition from the perspective of an auto-encoder. This auto-encoder was implemented as a stack of fully convolutional layers and was trained end-to-end by using a collection of training samples. We experimentally show that our method can successfully learn good image-acquisition and reconstruction strategies. With our method, light fields consisting of 5 × 5 or 8 × 8 images can be successfully reconstructed only from a few acquired images. Moreover, our method achieved superior performance over several state-of-the-art methods. We also applied our method to a real prototype camera to show that it is capable of capturing a real 3-D scene.

長原一

教授

コンピューテーショナルフォトグラフィ、コンピュータビジョンを専門とし実世界センシングや情報処理技術、画像認識技術の研究を行う。さらに、画像センシングにとどまらず様々なセンサに拡張したコンピュテーショナルセンシング手法の開発や高次元で冗長な実世界ビッグデータから意味のある情報を計測するスパースセンシングへの転換を目指す。