Light field (LF) contains the abundant spatial geometric information of the real-world scenes, and it can enhance the performance of the computer vision tasks. However, it is challenging to acquire LF images with high spatial resolution. So far, super-resolution (SR) techniques based on deep learning make insufficient use of frequency information, limiting the performance of LFSR. To address this issue, we propose a frequency-guided feature fusion network ( i.e. , LF-F 3 Net) for LFSR. To be specific, the proposed LF-F 3 Net is a dual-branch network. One branch employs multi-dimensional frequency feature extraction (MFFE) module to capture frequency information from individual views, while the other branch further integrates the extracted frequency information with spatial features through multi-dimensional spatial–frequency fusion (MSFF) module. Furthermore, we introduce a frequency loss to prevent the loss of critical frequency content during training, thereby maximizing the potential performance of network. The experimental results show that the LF-F 3 Net can significantly improve the SR performance and outperforms the state-of-the-art methods.
Yang et al. (2026) studied this question.