目的 全光场相机一次曝光获得4维光场信息,即2维空间信息和2维角度信息.然而,渲染获取图像的空间分辨率较低,角度分辨率也无法满足应用需求.针对此问题,提出一种边缘结构保持加权的超数字化全变分(BDTV)超分辨率重建算法.方法 将重建后图像与不同视角低分辨率图像的边缘拟合误差函数作为数据项的权重,并利用BDTV先验模型去噪和保持边缘.结果 为验证本文方法的有效性,将本文方法与其他方法进行了实验,其结果是无论是在视觉效果还是指标评价方面,本文方法的重建效果和边缘保持效果都优于其他方法,峰值信噪比提升约1 dB,结构相似性指数也有约0.01的提升.结论 本文算法不仅提高了全光场图像的空间分辨率和角度分辨率,同时也更好地保持了图像的边缘信息,特别是当深度图存在误差时,该算法的超分辨率重建和边缘保持效果更为明显.

Objective A plenoptic camera captures 4D light field information in a single photographic exposure, i.e., 2D spatial and 2D angular information. Nonetheless, the spatial resolutions of images rendered from light fields are significantly low. In addition, angular resolutions cannot meet application requirements given the limited number of viewpoints. In this paper, we present a new edge-preserved super-resolution method based on the weighted BDTV model.Method We apply the function of the residual errors in the edges of reconstructed images to weigh the data term in addition to different low-resolution viewpoint images. We also employ the previous BDTV model for denoising and edge preservation.Result Experimental results demonstrate that in terms of visual effects, the reconstruction and edge-preserving effects of the proposed method is superior to those of state-of-the-art methods. Peak signal-to-noise ratio rose by 1 dB while the measures of structural similarity in images increased by roughly 0.01.Conclusion Our approach not only improves the spatial and angular resolutions of rendered images but also preserves edge information. Although the depth estimate is inaccurate, the reconstruction and preservation effects of our method remain outstanding.