论文标题
深度多尺度的特征学习，用于散焦估计
Deep Multi-Scale Feature Learning for Defocus Blur Estimation
论文作者
论文摘要
本文提出了一种基于边缘的Defocus模糊估计方法，来自单个散焦图像。我们首先将处于深度不连续性的边缘（称为深度边缘，模糊估计值是模棱两可的）与位于近似恒定深度区域的边缘（称为模式边缘，对模糊估计值明确定义）。然后，我们仅在模式边缘估算散焦模糊量，并基于指导过滤器探索插值方案，该方案可防止在检测到的深度边缘上数据传播，以获得具有明确定义的对象边界的密集模糊图。这两个任务（边缘分类和模糊估计）均由深度卷积神经网络（CNN）执行，它们共享权重以从以边缘位置为中心的多尺度贴片中学习有意义的本地特征。对自然散落图像的实验表明，所提出的方法提出了超过最先进的方法（SOTA）方法的定性和定量结果，并且在运行时间和准确性之间存在良好的妥协。
This paper presents an edge-based defocus blur estimation method from a single defocused image. We first distinguish edges that lie at depth discontinuities (called depth edges, for which the blur estimate is ambiguous) from edges that lie at approximately constant depth regions (called pattern edges, for which the blur estimate is well-defined). Then, we estimate the defocus blur amount at pattern edges only, and explore an interpolation scheme based on guided filters that prevents data propagation across the detected depth edges to obtain a dense blur map with well-defined object boundaries. Both tasks (edge classification and blur estimation) are performed by deep convolutional neural networks (CNNs) that share weights to learn meaningful local features from multi-scale patches centered at edge locations. Experiments on naturally defocused images show that the proposed method presents qualitative and quantitative results that outperform state-of-the-art (SOTA) methods, with a good compromise between running time and accuracy.
