Depth from Defocus Technique Based on Cross Reblurring

Takemura, Kazumi; Yoshida, Toshiyuki

doi:10.1587/transinf.2019pcp0004

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…is article looks at the principle of autofocus to see if it can be used on a microscope. [11][12][13] is a method to obtain the depth information of the focus target through the defocused image so as to complete the autofocus. DFD method needs to obtain 2-3 frames of images with different defocusing degrees.…”

Section: Principle and Methods Of Autofocusmentioning

confidence: 99%

Automatic Focusing Method of Microscopes Based on Image Processing

Zhang

Yao

2021

Mathematical Problems in Engineering

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Microscope vision analysis is applied in many fields. The traditional way is to use the human eye to observe and manually focus to obtain the image of the observed object. However, with the observation object becoming more and more subtle, the magnification of the microscope is required to be larger and larger. The method of manual focusing cannot guarantee the best focusing position of the microscope in use. Therefore, in this paper, we are studying the existing autofocusing technology and the autofocusing method of microscope based on image processing, which is different from the traditional manual focusing method. The autofocusing method of microscope based on image processing does not need the information such as the target position and the focal length of optical system, to directly focus the collected image. First of all, in order to solve the problem of large computation and difficult real time of traditional wavelet based image sharpness evaluation algorithm, this paper proposes an improved wavelet based image sharpness evaluation algorithm; secondly, in view of the situation that the window selected by traditional focusing window selection method is fixed, this paper adopts an adaptive focusing window selection method to increase the focusing window. Finally, this paper studies the extremum search strategy. In order to avoid the interference of the local extremum in the focusing curve, this paper proposes an improved hill-climbing algorithm to achieve the accuracy of focusing search. The simulation results show that the improved wavelet transform image definition evaluation algorithm can improve the definition evaluation performance, and the improved mountain climbing algorithm can reduce the impact of local extremum and improve the accuracy of the search algorithm. All in all, it can be concluded that the method based on image processing proposed in this paper has a good focusing effect, which can meet the needs of anti-interference and extreme value search of microscope autofocus.

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Section: Principle and Methods Of Autofocusmentioning

confidence: 99%

Automatic Focusing Method of Microscopes Based on Image Processing

Zhang

Yao

2021

Mathematical Problems in Engineering

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“…5 At present, 3D surface morphology reconstruction methods of microparts based on 2D images obtained by MVS can be summarised as follows: shape from shading (SFS) method, 6 shape from texture (SFT) method, 7,8 shape from motion (SFM) method, 9,10 shape from focus (SFF) and shape from defocus (SFD) method. [11][12][13][14] Among them, SFS method obtains depth information by using shadow information or changes of shade, 6 SFT method obtains depth information by using texture details existing on the surface of observed object in image, 7,8 and SFM method obtains the depth information of observed object by taking images on multiple positions. 9,10 SFF and SFD methods obtain depth information of observed object through focusing and defocusing information in image.…”

Section: Introductionmentioning

confidence: 99%

“…9,10 SFF and SFD methods obtain depth information of observed object through focusing and defocusing information in image. [11][12][13][14] The above methods for obtaining depth information from 2D images have their respective scopes of applications and scenarios. For example, SFS method is suitable for observed objects with obvious light and dark changes on surface or sensitive to light.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, SFS method obtains depth information by using shadow information or changes of shade, 6 SFT method obtains depth information by using texture details existing on the surface of observed object in image, 7,8 and SFM method obtains the depth information of observed object by taking images on multiple positions 9,10 . SFF and SFD methods obtain depth information of observed object through focusing and defocusing information in image 11–14 . The above methods for obtaining depth information from 2D images have their respective scopes of applications and scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Depth estimation method of surface of micropart in microassembly space based on microscopic vision tomographic scanning images

Wang

Qiang

2021

Journal of Microscopy

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Three-dimensional (3D) morphology of microparts has an important influence on performance of microassembly system that mainly assembles microparts in millimetre and micron scale. Because 3D morphology of microparts cannot be accurately obtained by conventional microscopic vision system, a depth estimation method of surface of micropart in microassembly space based on microscopic vision tomographic scanning (MVTS) images is proposed in this paper. The proposed method uses the positions of pixels with the largest focus values in MVTS image to construct the isodepth contours of surface of micropart and obtains the depth values of micropart's surface at the positions of MVTS by assigning depth values to corresponding isodepth contours. The MVTS images are obtained by MVTS and pixels with the largest focus values in MVTS image are obtained by focus measurement of MVTS images of micropart in microassembly space. On these bases, 3D spatial interpolation method is applied to map depth value of space between adjacent isodepth contours and to obtain depth values of all surface of micropart. Simulation experiments are carried out to verify the proposed method by generating simulated MVTS image array from two simulation objects, and the influence parameters of the proposed method are analysed. In established experimental setup of microassembly that can realise MVTS, experimental verification for the proposed depth estimation method are carried out by using cone cavity and end jaws of microgripper. 3D morphologies of depth maps of cone cavity and end jaws of microgripper are registered with their respective CAD models using iterative nearest point registration algorithm to quantify accuracy of depth estimation. The research results show that 3D morphology of micropart can be obtained by the proposed method and has better accuracy than those by conventional shape from focus method. This method provides a new way to obtain the morphology of microparts and lays a foundation for improving

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