Flexible Hybrid Lenses Light Field Super-Resolution using Layered Refinement

In the hybrid lenses Light Field (LF) images, a high-resolution (HR) camera is in the center of the multiple low-resolution (LR) cameras, which introduces the beneficial high-frequency information for LF super-resolution. Therefore, how to effectively utilize the high-frequency information of the central view is the key issue for the hybrid lenses LF images super-resolution. In this paper, we propose a novel learning-based framework with Layered Refinement to super-resolve the hybrid lenses LF images. Specifically, we first transform the depth information of the scene into the layered position information, and refine it by complementing the high-frequency information of the HR central view to generate a high-quality representation of the depth information. Then, guided by high-quality depth representation, we propagate the information of the HR central view to the surrounding views accurately, and utilize the layered position information to maintain the occlusion relationship during the propagation. Moreover, as the generation of each layer position information is independent in our method, our trained model can flexibly adapt kinds of scenes with various disparity ranges without additional training. Experiments show that the proposed method outperforms the SOTA methods in kinds of scenes from simulated and real-world datasets with various disparity ranges. The code is available at ://github.com/racso10/LFHSR.