Open AccessDeep‐learning‐based depth estimation from light field images
Author(s) -
Schiopu I.,
Munteanu A.
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.2073
Subject(s) - epipolar geometry , mean squared error , artificial intelligence , light field , artificial neural network , computer science , block (permutation group theory) , pixel , minimum mean square error , deep learning , pattern recognition (psychology) , noise reduction , computer vision , mathematics , algorithm , image (mathematics) , statistics , estimator , geometry
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.