LOCAL-GLOBAL FEATURE AGGREGATION FOR LIGHT FIELD IMAGE SUPER-RESOLUTION

Deep convolutional neural networks (CNNs) have been widely explored in light field (LF) image super-resolution (SR) to achieve remarkable progress. However, most of the existing CNNs-based methods ignore the similarity of local neighbor views in the 4D LF data. Besides, due to the limitations of CNNs, these methods can't fully model the global spatial properties of the whole LF images. In this paper, we propose a network with Local-Global Feature Aggregation (LF-LGFA) to handle these problems for LF image SR. Specifically, the Local Aggregation Module is designed to incorporate the local angular information by utilizing the similarity of the local neighbor views' features in LF images. Moreover, the Global Aggregation Module is designed to capture long-range spatial information via row-wise and column-wise self-attention. Extensive experimental results on five public LF datasets demonstrate that our method achieves comparable results against state-of-the-art techniques.