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Journal Publication

Depth Estimation for Light-Field Images Using Stereo Matching and Convolutional Neural Networks

This publication appears in: Sensors

Authors: S. Rogge, I. Schiopu and A. Munteanu

Volume: 20

Issue: 21

Pages: 1-20

Publication Date: Oct. 2020


Abstract:

The paper presents a novel depth-estimation method for light-field (LF) images based on innovative multi-stereo matching and machine-learning techniques. In the first stage, a novel block-based stereo matching algorithm is employed to compute the initial estimation. The proposed algorithm is specifically designed to operate on any pair of sub-aperture images (SAIs) in the LF image and to compute the pair’s corresponding disparity map. For the central SAI, a disparity fusion technique is proposed to compute the initial disparity map based on all available pairwise disparities. In the second stage, a novel pixel-wise deep-learning (DL)-based method for residual error prediction is employed to further refine the disparity estimation. A novel neural network architecture is proposed based on a new structure of layers. The proposed DL-based method is employed to predict the residual error of the initial estimation and to refine the final disparity map. The experimental results demonstrate the superiority of the proposed framework and reveal that the proposed method achieves an average improvement of 15.65% in root mean squared error (RMSE), 43.62% in mean absolute error (MAE), and 5.03% in structural similarity index (SSIM) over machine-learning-based state-of-the-art methods.

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Miss Ségolène Rogge

+32 (0)02 629 169

srogge@etrovub.be

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Dr. Ionut Schiopu

+32 (0)02 629 169

ischiopu@etrovub.be

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Prof. Dr. Ir. Adrian Munteanu

+32 (0)02 629 168

acmuntea@etrovub.be

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