F2RNET: A Full-Resolution Representation Network For Biomedical Image Segmentation

Limited by the sensor resolution of plenoptic cameras, it is challenging to obtain spatial and angular high resolution light field (LF). in this paper, we propose a method for spatial and angular super-resolution (SR) simultaneously while preserving parallax structure, which generates a high resolution (HR) dense LF from a low resolution (LR) sparse one with detailed textures. The proposed framework consists of four parts, including a depth estimation module to get scene geometry, an image warping process to generate intermediate warped views, an epipolar plane image (EPI) generation module to recover structural features, and a spatial-angular SR module to fully explore the informative features in 4D LF. Finally, the high quality HR scenes can be effectively reconstructed. Experimental results over several LF datasets demonstrate that our approach not only outperforms the existing spatial and angular SR methods separately, but also achieves better quantitative and qualitative results than the one-stage and two-stage spatial-angular approaches.