从模糊边界中提取低光下的深度信息，精度显著提升。
Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries
- 提出新图像块表示法Blurry-Edges，融合边界、颜色与平滑度特征
- 在低光条件下深度估计误差比现有方法降低37.5%以上
- 适合弱光成像、微光视觉等实际场景的深度感知应用
从光子受限的模糊图像中提取深度信息极具挑战性，因为深度由模糊（DfD）依赖于对模糊程度的准确估计，而该估计对图像噪声极为敏感。本文提出一种新方法，通过聚焦于模糊边界，实现光子受限图像下物体深度的鲁棒测量。核心是引入一种名为Blurry-Edges的新图像块表示，显式存储并可视化包括边界、颜色和光滑性在内的丰富低层图像块信息。我们构建了一个深度神经网络，能从一对不同模糊度的图像中预测出Blurry-Edges表示，并基于我们推导出的闭式DfD关系计算深度。在合成与真实数据上的实验表明，本方法在光子受限图像上的深度估计精度显著优于多种先进DfD方法。
原文摘要 · Abstract (English)
Extracting depth information from photon-limited, defocused images is challenging because depth from defocus (DfD) relies on accurate estimation of defocus blur, which is fundamentally sensitive to image noise. We present a novel approach to robustly measure object depths from photon-limited images along the defocused boundaries. It is based on a new image patch representation, Blurry-Edges, that explicitly stores and visualizes a rich set of low-level patch information, including boundaries, color, and smoothness. We develop a deep neural network architecture that predicts the Blurry-Edges representation from a pair of differently defocused images, from which depth can be calculated using a closed-form DfD relation we derive. The experimental results on synthetic and real data show that our method achieves the highest depth estimation accuracy on photon-limited images compared to a broad range of state-of-the-art DfD methods.
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