Light Field Super-Resolution via Adaptive Feature Remixing

Keunsoo Ko, Yeong Jun Koh, Soonkeun Chang, Chang Su Kim

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


A novel light field super-resolution algorithm to improve the spatial and angular resolutions of light field images is proposed in this work. We develop spatial and angular super-resolution (SR) networks, which can faithfully interpolate images in the spatial and angular domains regardless of the angular coordinates. For each input image, we feed adjacent images into the SR networks to extract multi-view features using a trainable disparity estimator. We concatenate the multi-view features and remix them through the proposed adaptive feature remixing (AFR) module, which performs channel-wise pooling. Finally, the remixed feature is used to augment the spatial or angular resolution. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms on various light field datasets. The source codes and pre-trained models are available at LFSR-AFR

Original languageEnglish
Article number9394760
Pages (from-to)4114-4128
Number of pages15
JournalIEEE Transactions on Image Processing
Publication statusPublished - 2021


  • Light field
  • convolutional neural network (CNN)
  • feature remixing
  • super-resolution

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Light Field Super-Resolution via Adaptive Feature Remixing'. Together they form a unique fingerprint.

Cite this