CFA Handling and Quality Analysis for Compressive Light Field Camera


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.

ITE Transactions on Media Technology and Applications
Hajime Nagahara
Hajime Nagahara

He is working on computer vision and pattern recognition. His main research interests lie in image/video recognition and understanding, as well as applications of natural language processing techniques.