关键词: 光场, 深度估计, 极平面图, 视差, 散焦, 卷积神经网络

Abstract:

To solve the problem of low accuracy in traditional depth estimation methods of light field which is caused by using only one depth clue, a multi-input convolutional neural network combining disparity and defocus is proposed. This algorithm builds two depth clues by converting input images into EPI Volume and Refocus Volume, using the CNN to combine the EPI and defocus features into depth map. To overcome the lack of training data, data augmentation methods such as color transform, zoom, random gray scale and so on are adopted. Tests on HCI 4D light field benchmark dataset show that this method can achieve better result than traditional methods do in Mean Squared Error（MSE） and bad pixel ratio, also it is faster than traditional methods.

Key words: light field, depth estimation, Epipolar Plane Image（EPI）, disparity, defocus, Convolutional Neural Network（CNN）