Light field (LF) imaging, which captures both spatial and angular information of a scene, is undoubtedly beneficial to numerous applications. Although various techniques have been proposed for LF acquisition, achieving both angularly and spatially high-resolution LF remains a technology challenge. In this paper, a learning-based approach applied to 3D epipolar image (EPI) is proposed to reconstruct high-resolution LF. Through a 2-stage super-resolution framework, the proposed approach effectively addresses various LF super-resolution (SR) problems, i.e., spatial SR, angular SR, and angular-spatial SR. While the first stage provides flexible options to up-sample EPI volume to the desired resolution, the second stage, which consists of a novel EPI volume-based refinement network (EVRN), substantially enhances the quality of the high-resolution EPI volume. An extensive evaluation on 90 challenging synthetic and real-world light field scenes from 7 published datasets shows that the proposed approach outperforms state-of-the-art methods to a large extend for both spatial and angular super-resolution problem, i.e., an average peak signal to noise ratio improvement of more than 2.0 dB, 1.4 dB, and 3.14 dB in spatial SR $\times 2$, spatial SR $\times 4$, and angular SR respectively. The reconstructed 4D light field demonstrates a balanced performance distribution across all perspective images and presents superior visual quality compared to the previous works.
翻译:光场(LF)成像既捕捉现场的空间和角信息,也无疑有益于多种应用。虽然提议了各种技术来获取LF,但实现角和空间高分辨率LF仍然是一项技术挑战。在本文中,提议对3D上层图像(EPI)采用基于学习的方法,以重建高分辨率LF。通过一个两个阶段的超级分辨率框架,拟议方法有效地解决了各种LF超分辨率问题,即空间SR、角SR和角空间空间SR。虽然第一阶段为达到理想分辨率的上层和空间高分辨率LF提供了灵活选择,但第二阶段(EVPI 体)成像(EPI)成像(EPI)成像(EPI)成像(EVRN)成全新版本,大大提高了高分辨率ELI体卷的质量。对90个具有挑战性的合成和现实世界光场场场场场面(从7个已公布的数据集)进行了广泛的评价,显示拟议方法超越了状态和角法方法,在空间和角平面平面平面平面平面平面图像中,比平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面平面