IEEE Trans Image Process. 2016 Dec;25(12):5943-5956. doi: 10.1109/TIP.2016.2617460. Epub 2016 Oct 13.
Defocus map estimation (DME) is highly important in many computer vision applications. Nearly, all existing approaches for DME from a single image are based on a one-parameter defocus model, which does not allow for the variation of depth over edges. In this paper, a novel two-parameter model of defocused edges is proposed for DME from a single image. We can estimate the defocus amounts for each side of the edges through this proposed model, and the confidence that the edge is a pattern edge, where the depth remains the same over the edge, can be generated. Then, we modify the TV-L1 algorithm for structure-texture decomposition by taking advantage of this confidence to eliminate pattern edges while preserving structural ones. Finally, the defocus amounts estimated at the edge positions are used as initial values, and the structure component is employed as a guidance in the following Laplacian matting procedure to avoid the influence of pattern edges on the final defocus map. Experiment results show that the proposed method can effectively eliminate the influence of pattern edges compared with the state-of-art method. Furthermore, the estimated defocus map is feasible in applications of depth estimation and foreground/background segmentation.
散焦图估计（DME）在许多计算机视觉应用中非常重要。几乎所有现有的从单张图像进行DME的方法都基于单参数散焦模型，该模型不允许深度在边缘处发生变化。在本文中，提出了一种新颖的用于从单张图像进行DME的双参数散焦边缘模型。通过该模型，我们可以估计边缘两侧的散焦量，并生成边缘是图案边缘（即边缘上深度保持不变）的置信度。然后，我们利用此置信度修改用于结构纹理分解的TV-L1算法，以消除图案边缘同时保留结构边缘。最后，将在边缘位置估计的散焦量用作初始值，并在随后的拉普拉斯抠图过程中使用结构分量作为指导，以避免图案边缘对最终散焦图的影响。实验结果表明，与现有方法相比，该方法能有效消除图案边缘的影响。此外，估计的散焦图在深度估计和前景/背景分割应用中是可行的。
