概要
A light field can carry rich visual information of a real 3-D scene, leading to many attractive applications. However, the acquisition of a light field is challenging due to the large amount of data. In our previous work, we proposed an efficient method for this task using a coded-aperture camera with a convolutional neural network (CNN) which can computationally reconstruct a light field from several images acquired with different aperture patterns. In this work, we report two follow-up contributions to the previous work. First, we integrated a color filter array, which is common in RGB cameras, and the related color processing into the algorithm pipeline. This integration led to better reconstruction quality for color light fields. We then analyzed how the reconstruction quality obtained with our method was affected by the complexity of light fields. We also showed the possibility of using this analysis to predict the reconstruction quality from the acquired images.
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。