no code implementations • 20 Jan 2023 • Shansi Zhang, Nan Meng, Edmund Y. Lam
Depth estimation from light field (LF) images is a fundamental step for numerous applications.
no code implementations • 6 Sep 2022 • Shansi Zhang, Nan Meng, Edmund Y. Lam
Light field (LF) images containing information for multiple views have numerous applications, which can be severely affected by low-light imaging.
no code implementations • 6 Apr 2021 • Nan Meng, Yun-Bin Zhao
In this paper, we propose the so-called Newton-type optimal $k$-thresholding (NTOT) algorithm which is motivated by the appreciable performance of both Newton-type methods and the optimal $k$-thresholding technique for signal recovery.
no code implementations • 7 Sep 2020 • Nan Meng, Kai Li, Jianzhuang Liu, Edmund Y. Lam
This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images.
1 code implementation • 29 Mar 2020 • Nan Meng, Xiaofei Wu, Jianzhuang Liu, Edmund Y. Lam
In this paper, we propose a novel high-order residual network to learn the geometric features hierarchically from the LF for reconstruction.
1 code implementation • 3 Oct 2019 • Nan Meng, Hayden K. -H. So, Xing Sun, Edmund Y. Lam
We consider the problem of high-dimensional light field reconstruction and develop a learning-based framework for spatial and angular super-resolution.