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.

翻译：从光子受限的离焦图像中提取深度信息具有挑战性，因为离焦深度估计依赖于对离焦模糊的精确估计，而这一过程本质上对图像噪声极为敏感。本文提出一种新颖方法，能够从光子受限图像中沿离焦边界稳健地测量物体深度。该方法基于一种新的图像块表示——模糊边缘，该表示明确存储并可视化包含边界、颜色和平滑度在内的丰富低层次图像块信息。我们设计了一种深度神经网络架构，可从一对不同离焦程度的图像中预测模糊边缘表示，进而利用我们推导出的闭式离焦深度关系计算深度。在合成数据与真实数据上的实验结果表明，与多种先进的离焦深度估计方法相比，我们的方法在光子受限图像上实现了最高的深度估计精度。