Deep Spatial-angular Regularization for Light Field Imaging, Denoising, and Super-resolution

Mantang Guo, Junhui Hou*, Jing Jin, Jie Chen, Lap-Pui Chau

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

32 Citations (Scopus)

Abstract

Coded aperture is a promising approach for capturing the 4-D light field (LF), in which the 4-D data are compressively modulated into 2-D coded measurements that are further decoded by reconstruction algorithms. The bottleneck lies in the reconstruction algorithms, resulting in rather limited reconstruction quality. To tackle this challenge, we propose a novel learning-based framework for the reconstruction of high-quality LFs from acquisitions via learned coded apertures. The proposed method incorporates the measurement observation into the deep learning framework elegantly to avoid relying entirely on data-driven priors for LF reconstruction. Specifically, we first formulate the compressive LF reconstruction as an inverse problem with an implicit regularization term. Then, we construct the regularization term with a deep efficient spatial-angular separable convolutional sub-network in the form of local and global residual learning to comprehensively explore the signal distribution free from the limited representation ability and inefficiency of deterministic mathematical modeling. Furthermore, we extend this pipeline to LF denoising and spatial super-resolution, which could be considered as variants of coded aperture imaging equipped with different degradation matrices. Extensive experimental results demonstrate that the proposed methods outperform state-of-the-art approaches to a significant extent both quantitatively and qualitatively, i.e., the reconstructed LFs not only achieve much higher PSNR/SSIM but also preserve the LF parallax structure better on both real and synthetic LF benchmarks. The code will be publicly available at https://github.com/MantangGuo/DRLF.

Original languageEnglish
Pages (from-to)6094-6110
Number of pages17
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume44
Issue number10
Early online date8 Jun 2021
DOIs
Publication statusPublished - 1 Oct 2022

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • Light field
  • coded aperture
  • deep learning
  • optimization
  • observation model
  • denoising
  • spatial super-resolution
  • depth

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