Depth Estimation and Focus Recovery
Date Issued
2008
Date
2008
Author(s)
Lin, Yu-Che
Abstract
In this thesis, we discuss how to get depth values from image and how to recover a defocused image. We introduce the fundamentals of geometric optics and Fourier optics in parallel incident light hence it leads to the training methods on depth cues of docu-ments and ours.ost of depth recovery methods are simply based on camera focus and defocus. Among those approaches, they usually fall in a depth discontinuity problem. However, there are some methods which can solve such problems.inocular vision and monocular vision methods are two main directions to train images for estimating depths. One basic idea of binocular method comes from the stereo vision of human eyes. While there is two-side gazing on the same baseline we can sense that stereo from objects. mong the monocular methods, depth from focus (DFF) and depth from defocus (DFD) are the skills specifically.FF is used to estimate a more accurate depth value of the object. It could be im-plemented on medical images, for example, the diagnosis of the image from a laparo-scope. DFF uses Gaussian interpolation of different “degree of focus” on a particular pixel to approach an accurate depth value. To do this, we need a sequence of images and it is taken with different distances between the object and camera. As to the DFD, it is based on solving imaging model referring to different camera settings which include focal length, aperture diameter and distance between shoot to sensor.ecause image blurring is performed as not only a point property of image but a spatial degrading, all of those approaches require to define a spatial window (working window) where the blurring degree inside is same.ur proposed method will be introduced in the last two chapters. We suggest ap-plying the linear canonical transform (LCT) to estimate the depth value of the object and also recover the defocused image.he LCT has the property that it can simulate many optical effects using its four parameters. We use the LCT to derive and realize the output signal with the specific in-put signals, like Gaussian function and step function (edge). The key point is that we can get the relation between defocused input signals and depth values.hile in focus recovery, we also use LCT to simulate focus images from defo-cused one. Theoretically, through estimating an optical environment with a tiny aperture diameter can make effective focus images. However, the simulation on the LCT is a tough work. We will also discuss the simulation problems in detail in this thesis.
Subjects
depth estimation
focus
defocus
image restoration
linear canonical transform
Fourier optics
geometric optics
image blurring
Type
thesis
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