  Recent years have witnessed the great advances of deep neural networks (DNNs)
in light field (LF) image super-resolution (SR). However, existing DNN-based LF
image SR methods are developed on a single fixed degradation (e.g., bicubic
downsampling), and thus cannot be applied to super-resolve real LF images with
diverse degradation. In this paper, we propose a simple yet effective method
for real-world LF image SR. In our method, a practical LF degradation model is
developed to formulate the degradation process of real LF images. Then, a
convolutional neural network is designed to incorporate the degradation prior
into the SR process. By training on LF images using our formulated degradation,
our network can learn to modulate different degradation while incorporating
both spatial and angular information in LF images. Extensive experiments on
both synthetically degraded and real-world LF images demonstrate the
effectiveness of our method. Compared with existing state-of-the-art single and
LF image SR methods, our method achieves superior SR performance under a wide
range of degradation, and generalizes better to real LF images. Codes and
models are available at https://yingqianwang.github.io/LF-DMnet/.
