CN115096211A - Three-dimensional reduction method for extracting re-blurred confocal differential axial effective area - Google Patents
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Abstract
A three-dimensional reduction method for extracting a re-blurred confocal differential axial effective area comprises the steps of shooting three images of a focal plane image, a pre-focal image and a post-focal image; blurring the focal plane image, extracting a contour edge area in the focal plane image, and extracting a corresponding area of the contour edge area in the re-blurred image; respectively calculating edge gradients, performing ratio operation on the edge gray gradients and the re-blurred edge gray gradients, and calculating out-of-focus depth; extracting areas with the depth of the focal plane image area less than or equal to the out-of-focus distance of the pre-focus image and the post-focus image; performing difference making according to the effective areas extracted before and after the focus, and reducing the three-dimensional appearance of the effective measurement area in the field of view by using a confocal differential axial response curve; the invention realizes the extraction of the effective measurement area of confocal differential measurement, provides a new method for accurately restoring the three-dimensional morphology of confocal differential measurement, can be applied to different confocal measurement systems, and has certain universality.
Description
Technical Field
The invention relates to the field of optical measurement, in particular to a three-dimensional reduction method for extracting a re-blurred confocal differential axial effective area.
Background
The ultra-precise three-dimensional measurement technology is a core foundation and a key technology of modern precision manufacturing and advanced processing manufacturing technology, is widely applied to the fields of aerospace, national defense industry, biomedicine, communication engineering, microelectronics and the like, and the modern manufacturing industry puts forward requirements on high precision, large measurement range and rapidness for surface appearance measurement. The optical measurement method does not need to prepare a measurement sample in advance and does not need to contact the sample, so that the surface of the measured sample cannot be damaged; compared with a three-dimensional measurement method of a contact type and scanning probe microscope, the optical measurement method does not need a physical probe, so that the preparation and measurement of sample measurement are more flexible, the speed can be improved in a non-scanning measurement mode, and the real-time three-dimensional topography measurement and even the high-speed three-dimensional topography measurement can be realized; optical measurement methods do not require the use of probes to physically contact or contact as much as possible the sample surface and therefore do not cause permanent damage to the sample surface. Various optical three-dimensional surface measurement methods have been developed.
In confocal differential measurement, the three-dimensional morphology in the whole view field region cannot be restored because of the limited axial measurement range. Therefore, the extraction of the effective axial measurement area part in the field of view has very important significance for the real three-dimensional shape reduction of the measured sample, but no effective extraction method exists at present.
Disclosure of Invention
The invention mainly aims to overcome the defects in the prior art, provides a three-dimensional reduction method for extracting a re-blurred confocal differential axial effective region, realizes extraction of an effective measurement region for confocal differential measurement, provides a new accurate reduction method for realizing three-dimensional morphology of confocal differential measurement, can be applied to different confocal measurement systems, and has certain universality.
The invention adopts the following technical scheme:
a three-dimensional reduction method for extracting a re-blurred confocal differential axial effective area is realized by the following steps:
taking a focal plane image P with a camera 0 (x, y), pre-focal image P 1 (x, y), post-focal image P 2 (x, y) three images;
focal plane image P 0 (x, y) blurring to obtain a re-blurred image P 3 (x,y)；
Extracting a focal plane image P 0 Contour edge region P within (x, y) 0 ' (x, y) and extracts a re-blurred image P 3 Contour edge region in (x, y) corresponds to region P 3 '(x,y)；
Respectively calculating the contour edge regions P 0 ' (x, y) and the region P corresponding to the contour edge region 3 ' (x, y) edge gradient, resulting in an edge gray level gradient P 0 "(x, y) and re-blurred edge gray-scale gradient P 3 ”(x,y)；
Edge gray scale gradient P 0 "(x, y) and re-blurred edge gray-scale gradient P 3 "(x, y) performing ratio operation to obtain an edge gradient ratio sigma (x, y);
according to the edge gradient ratio sigma (x, y) and lens imaging formula
Calculating the defocusing depth, wherein k is a constant, and D is the clear aperture of the optical system; s is the distance between the image plane and the optical system; d f Is the object space focal length; Δ d is the defocus depth;
extracting a focal plane image P 0 (x, y) defocus depth Δ d is not more than image P before focus 1 (x, y), post-focal image P 2 (x, y) region P of defocus distance 1 ' (x, y) and P 2 '(x,y)；
According to the effective areas P extracted before and after the coke 1 ' (x, y) and P 2 ' (x, y), performing difference, and restoring the three-dimensional appearance of the effective measurement area in the field of view by using a confocal differential axial response curve.
Specifically, a camera is used to capture a focal plane image P 0 (x, y), pre-focus image P 1 (x, y), post-focal image P 2 (x, y) three images, and specific methods include but are not limited to:
point scanning confocal, line scanning confocal, or area array confocal;
specifically, the focus plane image P 0 (x, y) blurring to obtain a re-blurred image P 3 (x, y), the blur processing method includes but is not limited to:
the point spread function of the imaging system performs a convolution operation on the focal plane image, the convolution kernel including, but not limited to, a gaussian kernel, a cauchy kernel, or a gaussian-cauchy kernel.
Specifically, the focal plane image P is extracted 0 Contour edge region P within (x, y) 0 ' (x, y) and extracts a re-blurred image P 3 Contour edge region in (x, y) corresponds to region P 3 ' (x, y), contour edge region extraction methods include, but are not limited to:
sobel extraction method, roberts extraction method, canny extraction method, Laplacian extraction method, and morphological edge extraction method.
Specifically, the contour edge regions P are calculated separately 0 ' (x, y) and the region P corresponding to the contour edge region 3 ' (x, y) edge gradient, resulting in an edge gray level gradient P 0 "(x, y) and re-blurred edge gray-scale gradient P 3 "(x, y), edge gradient extraction methods include, but are not limited to:
and calculating the gradient change of the image edge by adopting a sobel or robert edge gradient operator or a threshold value edge extraction method, and calculating the gradient change of the image edge by directly utilizing the edge gradient operator or the threshold value edge extraction method.
Specifically, a confocal differential axial response curve is used for restoring the three-dimensional appearance of an effective measurement area in a field of view, and the confocal differential axial response curve restoring method comprises the following steps:
in confocal images, the axial light intensity satisfies the formula:
wherein u is the axial defocusing amount of an object space, and I (u) is an axial light intensity value; the axial differential measurement is to obtain two axial light intensity response curves deviating from the positive focal plane and equidistant before and after the focal plane and make a difference:
u F the deviation between the front and rear of the focusShifting; in the actual differential three-dimensional measurement, the difference between two pre-focus images and two post-focus images is obtained to obtain the gray difference value and the differential curve I T The linear region in (1) restores depth information of the focal plane image.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
the invention provides a three-dimensional reduction method for extracting a re-blurred confocal differential axial effective region, realizes extraction of a confocal differential measurement effective measurement region, provides a new accurate reduction method for realizing confocal differential measurement three-dimensional morphology, can be applied to different confocal measurement systems, and has certain universality.
Drawings
FIG. 1 is a flow chart of a three-dimensional reduction method for extracting a re-blurred confocal differential axial effective region according to an embodiment of the present invention;
FIG. 2 is a graph illustrating the relationship between the light intensity and the axial defocus of the sample according to the embodiment of the present invention;
fig. 3 is a simulation diagram of an axial differential measurement curve according to an embodiment of the present invention.
The invention is described in further detail below with reference to the figures and specific examples.
Detailed Description
The invention provides a three-dimensional reduction method for extracting a re-blurred confocal differential axial effective region, realizes extraction of a confocal differential measurement effective measurement region, provides a new accurate reduction method for realizing confocal differential measurement three-dimensional morphology, can be applied to different confocal measurement systems, and has certain universality.
As shown in fig. 1, a flow chart of a three-dimensional reduction method for extracting a re-blurred confocal differential axial effective region according to an embodiment of the present invention is specifically:
a three-dimensional reduction method for extracting a re-blurred confocal differential axial effective area is realized by the following steps:
s101: taking a focal plane image P with a camera 0 (x, y), pre-focal image P 1 (x, y) after-focusImage P 2 (x, y) three images;
specifically, a camera is used to capture a focal plane image P 0 (x, y), pre-focus image P 1 (x, y), post-focal image P 2 (x, y) three images, and specific methods include but are not limited to:
point scanning confocal, line scanning confocal, or area array confocal;
s102: focal plane image P 0 (x, y) blurring to obtain a re-blurred image P 3 (x,y)；
Specifically, the focal plane image P 0 (x, y) blurring to obtain a re-blurred image P 3 (x, y), the blur processing method includes but is not limited to:
the point spread function of the imaging system performs a convolution operation on the focal plane image, the convolution kernel including, but not limited to, a gaussian kernel, a cauchy kernel, or a gaussian-cauchy kernel.
S103: extracting a focal plane image P 0 Contour edge region P within (x, y) 0 ' (x, y) and extracts a re-blurred image P 3 Contour edge region in (x, y) corresponds to region P 3 '(x,y)；
Specifically, the focal plane image P is extracted 0 Contour edge region P within (x, y) 0 ' (x, y) and extracts a re-blurred image P 3 Contour edge region in (x, y) corresponds to region P 3 ' (x, y), contour edge region extraction methods include, but are not limited to:
a Sobel extraction method, a roberts extraction method, a canny extraction method, a Laplacian extraction method, and a morphological edge extraction method.
S104: respectively calculating the contour edge regions P 0 ' (x, y) and the region P corresponding to the contour edge region 3 ' (x, y) edge gradient, resulting in an edge gray level gradient P 0 "(x, y) and a re-blurred edge gray gradient P 3 ”(x,y)；
Specifically, the contour edge regions P are calculated separately 0 ' (x, y) and the region P corresponding to the contour edge region 3 ' (x, y) edge gradient, resulting in an edge gray level gradient P 0 "(x, y) and re-blurred edge gray-scale gradient P 3 "(x, y), edge gradient extractionThe extraction method includes but is not limited to:
and calculating the gradient change of the image edge by using a sobel, robert edge gradient operator or a threshold value edge extraction method, and calculating the gradient change of the image edge by directly using the edge gradient operator or the threshold value edge extraction method.
S105: the edge gray gradient P 0 "(x, y) and re-blurred edge gray-scale gradient P 3 "(x, y) performing ratio operation to obtain an edge gradient ratio sigma (x, y);
s106: according to the edge gradient ratio sigma (x, y) and lens imaging formula
Calculating the defocusing depth, wherein k is a constant, and D is the clear aperture of the optical system; s is the distance between the image plane and the optical system; d f Is the object space focal length; Δ d is the defocus depth;
the calculation method of the ratio comprises the following steps:
the focal plane image of the ideal imaging system is a clear image P (x, y), and the focal plane image P acquired by the real system 0 (x, y) can be viewed as an absolutely sharp image P (x, y) with an unknown standard deviation but very small value convolution h (x, y, σ), while a blurred image P 3 (x, y) is the image P 0 (x, y) once the standard deviation value h (x, y, sigma) is known to be large 1 ) By convolution operations, i.e. blurring the image P 3 (x, y) can be regarded as a clear image P (x, y) and two times of convolution operation P is carried out 3 (x,y)＝P 0 (x,y)*h(x,y,σ)*h(x,y,σ 1 )；
P 0 (x, y) and P 3 The blur ratio σ (x, y) of (x, y) can be expressed as:
finally, the product is processed
Where σ is 1 It is known that σ and
the sigma is the same variable, a functional relation between delta d and sigma (x, y) can be obtained, and the calculation of the defocusing depth of the focal plane image can be realized by calculating delta d through sigma (x, y) through calibrating the curve in advance.
S107: extracting a focal plane image P 0 (x, y) depth d of area is not more than image P before focus 1 (x, y), post-focal image P 2 (x, y) region P of defocus distance 1 ' (x, y) and P 2 '(x,y)；
S108: according to the effective areas P extracted before and after the coke 1 ' (x, y) and P 2 ' (x, y), performing difference, and restoring the three-dimensional appearance of the effective measurement area in the field of view by using a confocal differential axial response curve.
Specifically, a confocal differential axial response curve is used for restoring the three-dimensional appearance of an effective measurement area in a field of view, and the confocal differential axial response curve restoring method comprises the following steps:
in confocal images, the axial light intensity satisfies the formula:
wherein u is the axial defocusing amount of an object space, and I (u) is an axial light intensity value; the axial differential measurement is to obtain two axial light intensity response curves deviating from the positive focal plane and equidistant before and after the focal plane and make a difference:
uF is the offset of the coke before and after the coke; during actual differential three-dimensional measurement, two images before and after focus are obtained for subtraction through a gray difference value and a differential curve I T The linear region in (1) restores depth information of the focal plane image.
FIG. 2 is a graph showing the relationship between the light intensity and the axial defocus of the sample according to the embodiment of the present invention; fig. 3 is a simulation diagram of an axial differential measurement curve according to an embodiment of the present invention.
The invention provides a three-dimensional reduction method for extracting a re-blurred confocal differential axial effective region, realizes extraction of a confocal differential measurement effective measurement region, provides a new accurate reduction method for realizing confocal differential measurement three-dimensional morphology, can be applied to different confocal measurement systems, and has certain universality.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.
Claims (6)
1. A three-dimensional reduction method for extracting a re-blurred confocal differential axial effective area is characterized by comprising the following implementation steps:
taking a focal plane image P with a camera 0 (x, y), pre-focal image P 1 (x, y), post-focal image P 2 (x, y) three images;
focal plane image P 0 (x, y) blurring to obtain a re-blurred image P 3 (x,y)；
Extracting a focal plane image P 0 Contour edge region P within (x, y) 0 ' (x, y) and extracts a re-blurred image P 3 Contour edge region in (x, y) corresponds to region P 3 '(x,y)；
Respectively calculating the contour edge regions P 0 ' (x, y) and the region P corresponding to the contour edge region 3 ' (x, y) edge gradient, resulting in an edge gray level gradient P 0 "(x, y) and a re-blurred edge gray gradient P 3 ”(x,y)；
Edge gray scale gradient P 0 "(x, y) and a re-blurred edge gray gradient P 3 "(x, y) performing ratio operation to obtain an edge gradient ratio sigma (x, y);
according to the edge gradient ratio sigma (x, y) and lens imaging formula
Calculating the defocusing depth, wherein k is a constant, and D is the clear aperture of the optical system; s is image plane and lightLearning a system distance; d f Is the object space focal length; Δ d is the defocus depth;
extracting a focal plane image P 0 (x, y) defocus depth Δ d is not more than image P before focus 1 (x, y), post-focal image P 2 (x, y) region P of defocus distance 1 ' (x, y) and P 2 '(x,y)；
According to the effective areas P extracted before and after the coke 1 ' (x, y) and P 2 ' (x, y), performing difference, and restoring the three-dimensional appearance of the effective measurement area in the field of view by using a confocal differential axial response curve.
2. The method of claim 1 for three-dimensional reconstruction of the re-blurred confocal differential axial active area extraction, wherein: taking a focal plane image P with a camera 0 (x, y), pre-focus image P 1 (x, y), post-focal image P 2 (x, y) three images, and specific methods include but are not limited to:
point scanning confocal, line scanning confocal, or area array confocal.
3. The method of claim 1, wherein the focal plane image P is a three-dimensional image of the confocal differential axial effective region 0 (x, y) blurring to obtain a re-blurred image P 3 (x, y), the blur processing method includes but is not limited to:
the point spread function of the imaging system performs a convolution operation on the focal plane image, the convolution kernel including, but not limited to, a gaussian kernel, a cauchy kernel, or a gaussian-cauchy kernel.
4. The method of claim 1 for three-dimensional reconstruction of a re-blurred confocal differential axial active area extraction, comprising: extracting a focal plane image P 0 Contour edge region P within (x, y) 0 ' (x, y) and extracts a re-blurred image P 3 Contour edge region in (x, y) corresponds to region P 3 ' (x, y), contour edge region extraction methods include, but are not limited to:
sobel extraction method, roberts extraction method, canny extraction method, Laplacian extraction method, and morphological edge extraction method.
5. The method of claim 1 for three-dimensional reconstruction of a re-blurred confocal differential axial active area extraction, comprising: respectively calculating the contour edge regions P 0 ' (x, y) and the region P corresponding to the contour edge region 3 ' (x, y) edge gradient, resulting in an edge gray level gradient P 0 "(x, y) and a re-blurred edge gray gradient P 3 "(x, y), edge gradient extraction methods include, but are not limited to:
and calculating the gradient change of the image edge by using a sobel, robert edge gradient operator or a threshold value edge extraction method, and calculating the gradient change of the image edge by directly using the edge gradient operator or the threshold value edge extraction method.
6. The method of claim 1, wherein the three-dimensional shape of the effective measurement area in the field of view is restored by using a confocal differential axial response curve, and the method comprises:
in confocal images, the axial light intensity satisfies the formula:
wherein u is the axial defocusing amount of an object space, and I (u) is an axial light intensity value; the axial differential measurement is to obtain two axial light intensity response curves deviating from the positive focal plane and equidistant before and after the focal plane and make a difference:
u F the defocus distance before and after the focus; during actual differential three-dimensional measurement, two images before and after focus are obtained for subtraction through a gray difference value and a differential curve I T The linear region in (1) restores depth information of the focal plane image.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5804813A (en) * | 1996-06-06 | 1998-09-08 | National Science Council Of Republic Of China | Differential confocal microscopy |
| CN102636118A (en) * | 2012-04-13 | 2012-08-15 | 北京理工大学 | Laser three-differential cofocal theta imaging detection method |
| CN105758336A (en) * | 2016-05-11 | 2016-07-13 | 北京理工大学 | Reflective laser differential confocal curvature radius measuring method and device |
| CN109945803A (en) * | 2019-04-19 | 2019-06-28 | 北京理工大学 | Laterally subtract each other laser differential confocal cylindrical curvature radius measurement method |
| CN110849289A (en) * | 2019-12-11 | 2020-02-28 | 宁波五维检测科技有限公司 | Double-camera parallel confocal differential microscopic 3D morphology measurement device and method |
| CN111220090A (en) * | 2020-03-25 | 2020-06-02 | 宁波五维检测科技有限公司 | Line focusing differential color confocal three-dimensional surface topography measuring system and method |
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- 2022-06-22 CN CN202210710316.4A patent/CN115096211A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5804813A (en) * | 1996-06-06 | 1998-09-08 | National Science Council Of Republic Of China | Differential confocal microscopy |
| CN102636118A (en) * | 2012-04-13 | 2012-08-15 | 北京理工大学 | Laser three-differential cofocal theta imaging detection method |
| CN105758336A (en) * | 2016-05-11 | 2016-07-13 | 北京理工大学 | Reflective laser differential confocal curvature radius measuring method and device |
| CN109945803A (en) * | 2019-04-19 | 2019-06-28 | 北京理工大学 | Laterally subtract each other laser differential confocal cylindrical curvature radius measurement method |
| CN110849289A (en) * | 2019-12-11 | 2020-02-28 | 宁波五维检测科技有限公司 | Double-camera parallel confocal differential microscopic 3D morphology measurement device and method |
| CN111220090A (en) * | 2020-03-25 | 2020-06-02 | 宁波五维检测科技有限公司 | Line focusing differential color confocal three-dimensional surface topography measuring system and method |
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