Angular‐flexible network for light field image super‐resolution - Zhengyu Liang (Wiley, 2021)
Abstract Light field (LF) cameras capture scenes from multiple views and provide additional angular information for image super‐resolution (SR). Existing CNN‐based LF image SR methods commonly develop specific models for different angular resolutions. However, since the angular resolution can vary significantly with different LF devices, these methods have limited flexibility for real‐world applications. Here, an angular‐flexible network to use a single model to super‐resolve LF images of arbitrary angular resolution is proposed. In this method, spatial and angular feature extractors are designed to achieve angular‐flexible feature extraction, and develop a decouple‐and‐fuse module for SR reconstruction. Moreover, a mixed‐angular‐resolution training strategy is proposed to further enhance the angular flexibility. Experimental results on five public datasets demonstrate the state‐of‐the‐art performance of the method. Source codes are available at https://github.com/ZhengyuLiang24/LF‐AFnet. Here, an angular‐flexible network for light field image super‐resolution is proposed. The network can handle LFs captured by different kinds of devices with arbitrary angular resolutions. Experimental results demonstrate the effectiveness and superior performance of the method.