most citedLight Field Reconstruction Using Convolutional Network on EPI and Extended Applications

165 citations · 178 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG20217 cited

Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints

Xinlei Yi, Xiuxian Li, Tao Yang +3

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The…

cs.CV2021

Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network

Gaochang Wu, Yebin Liu, Lu Fang +1

The light field (LF) reconstruction is mainly confronted with two challenges, large disparity and the non-Lambertian effect. Typical approaches either address the large disparity c…

eess.IV2021165 cited

Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications

Gaochang Wu, Yebin Liu, Lu Fang +2

In this paper, a novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views. We indicate that the reconstructio…

math.OC2019

Distributed Bandit Online Convex Optimization with Time-Varying Coupled Inequality Constraints

Xinlei Yi, Xiuxian Li, Tao Yang +3

This paper considers the problem of distributed bandit online convex optimization with time-varying coupled inequality constraints. This problem can be defined as a repeated game b…

math.OC2019

Exponential Convergence for Distributed Smooth Optimization Under the Restricted Secant Inequality Condition

Xinlei Yi, Shengjun Zhang, Tao Yang +2

This paper considers the distributed smooth optimization problem in which the objective is to minimize a global cost function formed by a sum of local smooth cost functions, by usi…

cs.CV20196 cited

LapEPI-Net: A Laplacian Pyramid EPI structure for Learning-based Dense Light Field Reconstruction

Gaochang Wu, Yebin Liu, Lu Fang +1

For dense sampled light field (LF) reconstruction problem, existing approaches focus on a depth-free framework to achieve non-Lambertian performance. However, they trap in the trad…