Deep-learning-based depth estimation from light field images

Abstract

A novel deep-learning-based depth estimation method for light field images is introduced. The proposed method employs a novel neural network design to estimate the disparity of each pixel based on block patches extracted from epipolar plane images. The network output is further refined based on filtering and denoising algorithms. Experimental results demonstrate an average improvement of 34.35% in root mean squared error (RMSE) and 49.44% in mean squared error over machine learning-based state-of-the-art methods.