论文标题
通过4D重新采样去除光场雨滴
Light Field Raindrop Removal via 4D Re-sampling
论文作者
论文摘要
光场去除（LFRR）旨在恢复光场（LF）中雨滴遮盖的背景区域。与单个图像相比，LF通过定期和密集地采样场景提供了更多丰富的信息。由于雨滴的差异比LF的背景更大，因此在其他视图中可以看到大多数湿润的纹理细节。在本文中，我们通过直接利用输入Raindrop LF中无雨滴区域的互补像素信息提出了一个新颖的LFRR网络，该信息由重新采样模块和改进模块组成。具体而言，重新采样模块产生了一种新的LF，该LF通过重新采样位置预测和提议的4D插值而受到雨滴污染的较少污染。改进模块改善了完全阻塞的背景区域的恢复，并纠正了由4D插值引起的像素误差。此外，我们仔细构建了第一个真实场景LFRR数据集用于模型培训和验证。实验表明，所提出的方法可以有效地消除雨滴，并在背景恢复和查看一致性维护中实现最先进的性能。
The Light Field Raindrop Removal (LFRR) aims to restore the background areas obscured by raindrops in the Light Field (LF). Compared with single image, the LF provides more abundant information by regularly and densely sampling the scene. Since raindrops have larger disparities than the background in the LF, the majority of texture details occluded by raindrops are visible in other views. In this paper, we propose a novel LFRR network by directly utilizing the complementary pixel information of raindrop-free areas in the input raindrop LF, which consists of the re-sampling module and the refinement module. Specifically, the re-sampling module generates a new LF which is less polluted by raindrops through re-sampling position predictions and the proposed 4D interpolation. The refinement module improves the restoration of the completely occluded background areas and corrects the pixel error caused by 4D interpolation. Furthermore, we carefully build the first real scene LFRR dataset for model training and validation. Experiments demonstrate that the proposed method can effectively remove raindrops and achieves state-of-the-art performance in both background restoration and view consistency maintenance.
