The depth information is useful in many image processing applications. However, since taking a picture is a process of projection of a 3D scene onto a 2D imaging sensor, the depth information is embedded in the image. Extracting the depth information from the image is a challenging task. A guiding principle is that the level of blurriness due to defocus is related to the distance between the object and the focal plane. Based on this principle and the widely used assumption that Gaussian blur is a good model for defocus blur, we formulate the problem of estimating the spatially varying defocus blurriness as a Gaussian blur classification problem. We solved the problem by training a deep neural network to classify image patches into one of the 20 levels of blurriness. We have created a dataset of more than 500000 image patches of size 32x32 which are used to train and test several well-known network models. We find that MobileNetV2 is suitable for this application due to its low memory requirement and high accuracy. The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter. The result is a defocus map that carries the information of the degree of blurriness for each pixel. We compare the proposed method with state-of-the-art techniques and we demonstrate its successful applications in adaptive image enhancement, defocus magnification, and multi-focus image fusion.

中文翻译：

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单幅图像深度散焦估计及其应用

深度信息在许多图像处理应用中很有用。然而，由于拍照是将 3D 场景投影到 2D 成像传感器上的过程，因此深度信息被嵌入到图像中。从图像中提取深度信息是一项具有挑战性的任务。一个指导原则是，由于散焦造成的模糊程度与物体与焦平面之间的距离有关。基于这一原理和广泛使用的假设，即高斯模糊是散焦模糊的良好模型，我们将估计空间变化散焦模糊的问题表述为高斯模糊分类问题。我们通过训练深度神经网络将图像块分类为 20 个模糊级别之一来解决这个问题。我们创建了一个包含超过 500000 个大小为 32x32 的图像块的数据集，用于训练和测试几个著名的网络模型。我们发现 MobileNetV2 适合这种应用，因为它的内存要求低，精度高。训练后的模型用于确定补丁模糊度，然后通过应用迭代加权引导滤波器对其进行细化。结果是一个散焦图，其中包含每个像素的模糊程度信息。我们将所提出的方法与最先进的技术进行比较，并展示了其在自适应图像增强、散焦放大和多焦点图像融合方面的成功应用。训练后的模型用于确定补丁模糊度，然后通过应用迭代加权引导滤波器对其进行细化。结果是一个散焦图，其中包含每个像素的模糊程度信息。我们将所提出的方法与最先进的技术进行比较，并展示了其在自适应图像增强、散焦放大和多焦点图像融合方面的成功应用。训练后的模型用于确定补丁模糊度，然后通过应用迭代加权引导滤波器对其进行细化。结果是一个散焦图，其中包含每个像素的模糊程度信息。我们将所提出的方法与最先进的技术进行比较，并展示了其在自适应图像增强、散焦放大和多焦点图像融合方面的成功应用。