利用2维离焦图像恢复景物的3维深度信息是计算机视觉中一个重要的研究方向。但是，在获取不同程度的离焦图像时，必须改变摄像机参数，例如，调节摄像机的焦距、像距或者光圈大小等。而在一些需要高倍放大观测的场合，使用的高倍精密摄像机的景深非常小，任何摄像机参数的改变都会对摄像机产生破坏性的后果，这在很大程度上限制了当前许多离焦深度恢复算法的应用范围。因此，提出了一种新的通过物距变化恢复景物全局深度信息的方法。首先，改变景物的物距获取两幅离焦程度不同的图像，然后，利用相对模糊度及热辐射方程建立模糊成像模型，最后，将景物深度信息的计算转化成一个动态优化问题并求解，获得全局景物深度信息。该方法不需改变任何摄像机参数或者计算景物的清晰图像，操作简单。仿真试验和误差分析结果表明，该方法可以实现高精度的深度信息恢复，适合应用于微纳米操作、高精度快速检测等对摄像机参数改变较为敏感的场合。

Reconstructing 3D depth information from 2D defocus images is one of the top important research topics in computer vision. However, existing methods need to change the camera parameters, such as the focal length of the lens, the distance of the focused image from the lens plane and the radius of the lens, to attain the defocus images of different blurring degree. Unfortunately, in some cases with high level of magnification cameras, any change of any parameter will destroy the cameras drastically, so the application field of many existent algorithms is strictly restricted. Therefore, in this paper, a novel Depth from Defocus (DFD) method is proposed to solve this problem. First, two different blurred images are captured through changing depth. Second, the relation between depth and blurring is discussed based on the blurred imaging model obtained from the concept of relative blurring and the diffusion equation. Finally, the depth reconstruction is completed by solving an optimization problem. This proposed algorithm which does not need change any camera parameters or compute the focus image is easy to be realized. What’s more, the results of simulations and error analysis show that this method can reconstruct depth information with high precision and can be used in micro/nano manipulation and fast detection which are sensitive to camera parameters.