Light field (LF) cameras record both intensity and directions of light rays in a scene with a single exposure. Due to the trade-off between spatial and angular dimensions, the spatial resolution of LF images is limited, so super-resolution is widely studied. Pixels follow linear coordinate projection across views in LF images. Hence, auxiliary views nearer to the target view are generally more effective for use in super-resolution. In this paper, an LF-pyramid is proposed based on an angular-distance constraint for discriminatively exploiting auxiliary views. From views of different layers in an LF-pyramid, complementary features of different effectiveness can be extracted. However, shapes of LF-pyramids change for target views with different angular positions. To fully exploit an LF-pyramid, we introduce a pyramid-angular-constraint network for LF super-resolution (LF-PACNet). Specifically, to handle an arbitrary number of views in each layer, an intra-pyramid-layer feature extraction module is designed, which treats all views in the same layer equally in complementary information extraction. Then, to deal with an arbitrary number of layers, a recurrent cross-pyramid-layer feature complementation module is constructed, which discriminatively complements the target view with high-frequency details. Extensive experiments on public datasets demonstrate state-of-the-art performance for our method, both visually and numerically, especially for datasets with large disparities.

Light field (LF) cameras record multiple perspectives by a sparse sampling of real scenes, and these perspectives provide complementary information. This information is beneficial to LF super-resolution (LFSR). Compared with traditional single-imagesuper-resolution, LF can exploit parallax structure and perspective correlation among different LF views. Furthermore, the performance of existing methods are limited as they fail to deeply explore the complementary information across LF views. In this paper, we propose a novel network, called the light field complementary-view feature attention network (LF-CFANet), to improve LFSR by dynamically learning the complementary information in LF views. Specifically, we design a residual complementary-view spatial and channel attention module (RCSCAM) to effectively interact with complementary information between complementary views. Moreover, RCSCAM captures the relationships between different channels, and it is able to generate informative features for reconstructing LF images while ignoring redundant information. Then, a maximum-difference information supplementary branch (MDISB) is used to supplement information from the maximum-difference angular positions based on the geometric structure of LF images. This branch also can guide the process of reconstruction. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method. The proposed LF-CFANet has a more advanced reconstruction performance that displays faithful details with higher SR accuracy than state-of-the-art methods.