Learning to capture light fields through a coded aperture camera


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.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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.