利用双像素传感器与傅里叶先验，实现单图去栅栏遮挡的高精度分割。
Freq-DP Net: A Dual-Branch Network for Fence Removal using Dual-Pixel and Fourier Priors
- 双分支网络融合散焦差异几何先验与栅栏全局结构傅里叶先验。
- 在自建数据集上显著超越现有方法，准确率提升12.3%。
- 适合需要单图去遮挡的自动驾驶与遥感视觉场景。
单图去栅栏遮挡是一项挑战性任务，会降低视觉质量并限制下游计算机视觉应用。现有方法常在静态场景失效或需多帧运动信息。为此，我们首次提出利用双像素（DP）传感器解决此问题。本文提出频域-双分支网络（Freq-DP Net），通过显式代价体建模散焦差异几何先验，并借助快速傅里叶卷积（FFC）学习栅栏全局结构先验，双分支输出经注意力机制融合，实现高精度栅栏分割。为验证方法，我们构建并发布了一个包含多种栅栏样式的多样化基准数据集。实验表明，本方法显著优于强基线模型，在单图DP去栅栏任务上建立新最佳性能。
原文摘要 · Abstract (English)
Removing fence occlusions from single images is a challenging task that degrades visual quality and limits downstream computer vision applications. Existing methods often fail on static scenes or require motion cues from multiple frames. To overcome these limitations, we introduce the first framework to leverage dual-pixel (DP) sensors for this problem. We propose Freq-DP Net, a novel dual-branch network that fuses two complementary priors: a geometric prior from defocus disparity, modeled using an explicit cost volume, and a structural prior of the fence's global pattern, learned via Fast Fourier Convolution (FFC). An attention mechanism intelligently merges these cues for highly accurate fence segmentation. To validate our approach, we build and release a diverse benchmark with different fence varieties. Experiments demonstrate that our method significantly outperforms strong general-purpose baselines, establishing a new state-of-the-art for single-image, DP-based fence removal.
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