Abstract Defocus map estimation (DME) is very useful in many computer vision applications and has drawn much attention in recent years. Edge-based DME methods can generate sharp defocus discontinuities but usually suffer from textures of the input image. Region-based methods are free of textures but cannot catch the defocus discontinuities very well. In this paper, we propose a DME method combining edge-based and region-based methods together to keep their respective advantages while eliminating the shortcomings. The combination is achieved via regression tree fields (RTF). In an RTF, the input feature and the linear basis are of vital importance. For our RTF, they are obtained as follows. (i) Two orthogonal gradient operators with the corresponding subsets of Gabor filters are employed in localized 2D frequency analysis to generate accurate likelihood, and the first K highest local maximums of likelihood are sent to an RTF as input feature. (ii) At the same time, the input image is processed by three edge-based methods and the results serve as the linear basis of RTF. The experiments demonstrate that the proposed method outperforms state-of-the-art DME methods. Moreover, the proposed method can be readily applied to defocused image deblurring and defocus blur detection.

中文翻译：

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使用改进的似然特征和基于边缘的基础从单个图像估计散焦图

摘要 散焦图估计（DME）在许多计算机视觉应用中非常有用，近年来引起了广泛关注。基于边缘的 DME 方法可以产生清晰的散焦不连续性，但通常会受到输入图像纹理的影响。基于区域的方法没有纹理，但不能很好地捕捉散焦不连续性。在本文中，我们提出了一种将基于边缘和基于区域的方法结合在一起的 DME 方法，以保持各自的优点，同时消除缺点。该组合是通过回归树字段 (RTF) 实现的。在 RTF 中，输入特征和线性基础至关重要。对于我们的 RTF，它们是按如下方式获得的。(i) 在局部二维频率分析中采用两个正交梯度算子和相应的 Gabor 滤波器子集来生成准确的似然，并将前 K 个最高的似然局部最大值作为输入特征发送到 RTF。(ii) 同时，输入图像经过三种基于边缘的方法处理，结果作为 RTF 的线性基础。实验表明，所提出的方法优于最先进的 DME 方法。此外，所提出的方法可以很容易地应用于散焦图像去模糊和散焦模糊检测。实验表明，所提出的方法优于最先进的 DME 方法。此外，所提出的方法可以很容易地应用于散焦图像去模糊和散焦模糊检测。实验表明，所提出的方法优于最先进的 DME 方法。此外，所提出的方法可以很容易地应用于散焦图像去模糊和散焦模糊检测。