Suppr超能文献

角度欠采样光场的混叠检测与减少方案。

Aliasing Detection and Reduction Scheme on Angularly Undersampled Light Fields.

出版信息

IEEE Trans Image Process. 2017 May;26(5):2103-2115. doi: 10.1109/TIP.2017.2668613. Epub 2017 Feb 13.

Abstract

When using plenoptic camera for digital refocusing, angular undersampling can cause severe (angular) aliasing artifacts. Previous approaches have focused on avoiding aliasing by pre-processing the acquired light field via prefiltering, demosaicing, reparameterization, and so on. In this paper, we present a different solution that first detects and then removes angular aliasing at the light field refocusing stage. Different from previous frequency domain aliasing analysis, we carry out a spatial domain analysis to reveal whether the angular aliasing would occur and uncover where in the image it would occur. The spatial analysis also facilitates easy separation of the aliasing versus non-aliasing regions and angular aliasing removal. Experiments on both synthetic scene and real light field data sets (camera array and Lytro camera) demonstrate that our approach has a number of advantages over the classical prefiltering and depth-dependent light field rendering techniques.

摘要

在使用光场相机进行数字重聚焦时,角度欠采样会导致严重的(角度)混叠伪像。以前的方法主要通过预处理采集的光场来避免混叠,例如预滤波、去马赛克、重新参数化等。在本文中,我们提出了一种不同的解决方案,即在光场重聚焦阶段首先检测然后去除角度混叠。与以前的频域混叠分析不同,我们进行了空间域分析,以揭示角度混叠是否会发生以及它会在图像中的哪个位置发生。空间分析还便于轻松区分混叠和非混叠区域以及角度混叠的去除。对合成场景和真实光场数据集(相机阵列和 Lytro 相机)的实验表明,与经典的预滤波和深度相关的光场渲染技术相比,我们的方法具有许多优势。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验