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

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

SUBMITTER: Rogge S 

PROVIDER: S-EPMC7663356 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature