CN113487508A - Method and device for dynamically adjusting picture definition of digital slice scanner - Google Patents
Method and device for dynamically adjusting picture definition of digital slice scanner Download PDFInfo
- Publication number
- CN113487508A CN113487508A CN202110781905.7A CN202110781905A CN113487508A CN 113487508 A CN113487508 A CN 113487508A CN 202110781905 A CN202110781905 A CN 202110781905A CN 113487508 A CN113487508 A CN 113487508A
- Authority
- CN
- China
- Prior art keywords
- kernel
- current
- image
- calculating
- definition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 230000000007 visual effect Effects 0.000 claims abstract description 76
- 238000001914 filtration Methods 0.000 claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims description 19
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000003707 image sharpening Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G06T5/90—
-
- G06T5/70—
Abstract
The invention discloses a method and a device for dynamically adjusting the image definition of a digital slice scanner, wherein the method comprises the following steps: acquiring the focusing visual field of the digital slice, and calculating the average definition of all the focusing visual fields; calculating a kernel of the current view image; carrying out average adjustment on the kernel of the current view image according to the frame view; calculating the adjustment intensity of the current visual field according to the average adjusted kernel; and calculating the image with the adjusted definition according to the adjustment intensity of the current visual field. The invention processes the image through median filtering and calculates and adjusts the intensity based on the kernel, thereby obtaining the image with enhanced definition, solving the problem of unbalanced definition caused by an algorithm compensation mode in a digital slice scanner, and providing a dynamic image definition compensation method based on the current image definition and adjacent image sharpening parameters.
Description
Technical Field
The invention relates to a method and a device for dynamically adjusting the image definition of a digital slice scanner, belonging to the technical field of digital slice image processing.
Background
Digital slice scanners are devices that digitize physical slices, the main principle being to form panoramic digital slices using a moving structure and an industrial camera. The method is divided into an area-array camera scanner and a line-array camera scanner according to different types of cameras. The line scan camera collects images in a column or row mode and then splices the column images or the row images; the area-array camera acquires images according to a fixed visual field and then forms a panoramic digital slice by registration and splicing.
The scanning process of the digital slice scanner based on the area-array camera comprises the following steps: separating a sample area from a preview navigation map, and finding all fields of view to be scanned according to the corresponding relation between the preview map and a microscopic light path; finding a corresponding focused field of view from the scanned field of view; after focusing the focusing visual fields, diffusing the focused positions to each scanning visual field; and acquiring images of each visual field, and forming a digital slice after registration and splicing.
In the slice digitalization process, due to the fact that the thicknesses of slice tissues at different positions are different, mechanical shaking and vibration exist in the scanning process; the visual field definition collected by the area-array camera is difficult to keep consistent. In the existing digital slice scanner, the image definition is enhanced to a certain extent by using a sharpening mode, so that a certain effect can be achieved. However, in the sharpening process by using the algorithm, all images adopt uniform sharpening coefficients, so that the situation of large difference of definition occurs, and the sharpening effect is difficult to ensure.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method and an apparatus for dynamically adjusting image sharpness of a digital slice scanner, which can solve the problem of uneven image sharpness in the digital slice scanner. The technical scheme adopted for solving the technical problems is as follows:
in a first aspect, an embodiment of the present invention provides a method for dynamically adjusting picture sharpness of a digital slice scanner, including:
acquiring the focusing visual field of the digital slice, and calculating the average definition of all the focusing visual fields;
calculating a kernel of the current view image;
carrying out average adjustment on the kernel of the current view image according to the frame view;
calculating the adjustment intensity of the current visual field according to the average adjusted kernel;
and calculating the image with the adjusted definition according to the adjustment intensity of the current visual field.
As a possible implementation manner of this embodiment, the acquiring the focused fields of view of the digital slice and calculating the average sharpness of all the focused fields of view includes:
scanning the physical slide by using a digital slide scanner to obtain a sample area of the digital slide;
dividing a sample region of the digital slice to obtain scanning field of view information;
selecting a focusing visual field, and focusing the focusing visual field;
the mean sharpness for all focused views was calculated using the standard deviation method.
As a possible implementation manner of this embodiment, the kernel for calculating the current-view image includes:
calculating the definition of the current view image;
calculating a kernel of the current view image:
kCurrentSize＝(1-(viewClarity-focusClarity)/focusClarity)*10
wherein, kCurrentSize is the kernel size of the current view image, viewClarity is the definition of the current view image, focusClarity is the average definition of all focused views;
an odd number is calculated for kCurrentSize, kCurrentSize ═ ceil (kCurrentSize)/2 × 2+ 1.
As a possible implementation manner of this embodiment, the averagely adjusting the kernel of the current-view image according to the frame view includes:
judging whether the current view has an upper view, if so, taking out a kernel of the current view, and carrying out average adjustment on the kernel of the current view;
judging whether the current view has a lower view, if so, taking out a kernel of the current view, and carrying out average adjustment on the kernel of the current view;
judging whether the current view has a left view, if so, taking out a kernel of the current view, and carrying out average adjustment on the kernel of the current view;
the formula for carrying out average adjustment on the current visual field kernel is as follows:
kCurrentSize＝kCurrentSize*(iNum/(iNum+1))+EdgeSize(1.0/(iNum+1.0))
wherein EdgeSize is the kernel in the top or bottom or left view.
As a possible implementation manner of this embodiment, the calculating the adjustment strength of the current field of view according to the average adjusted kernel includes:
calculating the odd number of the kCurrentSize;
calculating the adjustment intensity of the current visual field:
dCurrentDepth＝kCurrentSize*0.09；
wherein dCurrentDepth is the adjustment strength of the current field of view, and kCurrentSize is the kernel size of the current field of view image.
As a possible implementation manner of this embodiment, the calculating the image with adjusted sharpness according to the adjustment intensity of the current field includes:
performing median filtering on the original image by using the kernel of the current view image;
calculating a visual field image after sharpness enhancement:
resultCurrentImage＝viewPicture*(2*dCurrentDepth)-blurImage*(2*dCurrentDepth-1)；
wherein, the restcurrentimage is the view image after the definition enhancement, the viewPicture is the current view image, the dCurrentDepth is the adjustment intensity of the current view, and the blurrimage is the view image after the median filtering.
In a second aspect, an apparatus for dynamically adjusting image sharpness of a digital slice scanner provided in an embodiment of the present invention includes:
the average definition calculation module is used for acquiring the focusing visual field of the digital slice and calculating the average definition of all the focusing visual fields;
the kernel calculation module is used for calculating the kernel of the current view image;
the kernel adjusting module is used for carrying out average adjustment on the kernels of the current view images according to the frame views;
the adjustment intensity calculation module is used for calculating the adjustment intensity of the current visual field according to the average adjusted kernel;
and the image adjusting module is used for calculating the image with the adjusted definition according to the adjustment intensity of the current visual field.
As a possible implementation manner of this embodiment, the average sharpness calculating module includes:
the physical slide scanning module is used for scanning a physical slide by using a digital slide scanner to obtain a sample area of a digital slide;
the sample region segmentation module is used for segmenting a sample region of the digital slice to obtain scanning field of view information;
the focusing module is used for selecting a focusing visual field and focusing the focusing visual field;
and the standard deviation module is used for calculating the average definition of all focused visual fields by using a standard deviation method.
As a possible implementation manner of this embodiment, the kernel calculation module includes:
the current definition calculating module is used for calculating the definition of the current visual field image;
the current kernel calculation module is used for calculating the kernel of the current view image:
kCurrentSize＝(1-(viewClarity-focusClarity)/focusClarity)*10
wherein, kCurrentSize is the kernel size of the current view image, viewClarity is the definition of the current view image, focusClarity is the average definition of all focused views;
and an odd number taking module for taking an odd number of the kCurrentSize to calculate:
kCurrentSize＝ceil(kCurrentSize)/2*2+1。
as a possible implementation manner of this embodiment, the kernel adjusting module includes:
the upper view judgment module is used for judging whether the upper view exists in the current view, if so, taking out the kernel of the current view, and carrying out average adjustment on the kernel of the current view;
the lower view judgment module is used for judging whether the current view has a lower view, if so, taking out the kernel of the current view and carrying out average adjustment on the kernel of the current view;
the left view judging module is used for judging whether the current view has a left view, if so, taking out the kernel of the current view and carrying out average adjustment on the kernel of the current view;
the formula for carrying out average adjustment on the current visual field kernel is as follows:
kCurrentSize＝kCurrentSize*(iNum/(iNum+1))+EdgeSize(1.0/(iNum+1.0))
wherein EdgeSize is the kernel in the top or bottom or left view.
As a possible implementation manner of this embodiment, the adjustment strength calculation module is specifically configured to:
calculating the odd number of the kCurrentSize;
calculating the adjustment intensity of the current visual field:
dCurrentDepth＝kCurrentSize*0.09；
wherein dCurrentDepth is the adjustment strength of the current field of view, and kCurrentSize is the kernel size of the current field of view image.
As a possible implementation manner of this embodiment, the image adjusting module includes:
the median filtering module is used for performing median filtering on the original image by utilizing the kernel of the current view image;
the image enhancement module is used for calculating the vision field image after the definition enhancement:
resultCurrentImage＝viewPicture*(2*dCurrentDepth)-blurImage*(2*dCurrentDepth-1)；
wherein, the restcurrentimage is the view image after the definition enhancement, the viewPicture is the current view image, the dCurrentDepth is the adjustment intensity of the current view, and the blurrimage is the view image after the median filtering.
The technical scheme of the embodiment of the invention has the following beneficial effects:
since the area-array camera is based on the acquisition and splicing of one-view-field-of-view acquired images, it is necessary to ensure that the sharpening coefficients of adjacent images do not differ too much, otherwise, visible differences occur. Therefore, the invention processes the image through median filtering and calculates and adjusts the intensity based on the kernel, thereby obtaining the image with enhanced definition, solving the problem of unbalanced definition caused by an algorithm compensation mode in a digital slice scanner and providing a dynamic image definition compensation method based on the current image definition and adjacent image sharpening parameters.
Description of the drawings:
FIG. 1 is a flow diagram illustrating a method for dynamic adjustment of picture sharpness for a digital slice scanner in accordance with an exemplary embodiment;
fig. 2 is a block diagram illustrating an apparatus for dynamic adjustment of picture sharpness for a digital slice scanner in accordance with an exemplary embodiment.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
Fig. 1 is a flow chart illustrating a method for dynamic adjustment of picture sharpness for a digital slice scanner in accordance with an exemplary embodiment. As shown in fig. 1, a method for dynamically adjusting image sharpness of a digital slice scanner according to an embodiment of the present invention includes:
acquiring the focusing visual field of the digital slice, and calculating the average definition of all the focusing visual fields;
calculating a kernel of the current view image;
carrying out average adjustment on the kernel of the current view image according to the frame view;
calculating the adjustment intensity of the current visual field according to the average adjusted kernel;
and calculating the image with the adjusted definition according to the adjustment intensity of the current visual field.
As a possible implementation manner of this embodiment, the acquiring the focused fields of view of the digital slice and calculating the average sharpness of all the focused fields of view includes:
scanning the physical slide by using a digital slide scanner to obtain a sample area of the digital slide;
dividing a sample region of the digital slice to obtain scanning field of view information;
selecting a focusing visual field, and focusing the focusing visual field;
the mean sharpness for all focused views was calculated using the standard deviation method.
As a possible implementation manner of this embodiment, the kernel for calculating the current-view image includes:
calculating the definition of the current view image;
calculating a kernel of the current view image:
kCurrentSize＝(1-(viewClarity-focusClarity)/focusClarity)*10
wherein, kCurrentSize is the kernel size of the current view image, viewClarity is the definition of the current view image, focusClarity is the average definition of all focused views;
an odd number is calculated for kCurrentSize, kCurrentSize ═ ceil (kCurrentSize)/2 × 2+ 1.
As a possible implementation manner of this embodiment, the averagely adjusting the kernel of the current-view image according to the frame view includes:
judging whether the current view has an upper view, if so, taking out a kernel of the current view, and carrying out average adjustment on the kernel of the current view;
judging whether the current view has a lower view, if so, taking out a kernel of the current view, and carrying out average adjustment on the kernel of the current view;
judging whether the current view has a left view, if so, taking out a kernel of the current view, and carrying out average adjustment on the kernel of the current view;
the formula for carrying out average adjustment on the current visual field kernel is as follows:
kCurrentSize＝kCurrentSize*(iNum/(iNum+1))+EdgeSize(1.0/(iNum+1.0))
wherein EdgeSize is the kernel in the top or bottom or left view.
As a possible implementation manner of this embodiment, the calculating the adjustment strength of the current field of view according to the average adjusted kernel includes:
calculating the odd number of the kCurrentSize;
calculating the adjustment intensity of the current visual field:
dCurrentDepth＝kCurrentSize*0.09；
wherein dCurrentDepth is the adjustment strength of the current field of view, and kCurrentSize is the kernel size of the current field of view image.
As a possible implementation manner of this embodiment, the calculating the image with adjusted sharpness according to the adjustment intensity of the current field includes:
performing median filtering on the original image by using the kernel of the current view image;
calculating a visual field image after sharpness enhancement:
resultCurrentImage＝viewPicture*(2*dCurrentDepth)-blurImage*(2*dCurrentDepth-1)；
wherein, the restcurrentimage is the view image after the definition enhancement, the viewPicture is the current view image, the dCurrentDepth is the adjustment intensity of the current view, and the blurrimage is the view image after the median filtering.
In this embodiment, an image is processed through median filtering, and the intensity is adjusted based on kernel calculation, so that an image with enhanced definition is obtained, the problem of unbalanced definition of the image caused by an algorithm compensation mode in a digital slice scanner is solved, and a dynamic image definition compensation method based on the current image definition and adjacent image sharpening parameters is provided.
As shown in fig. 2, an apparatus for dynamically adjusting image sharpness of a digital slice scanner according to an embodiment of the present invention includes:
the average definition calculation module is used for acquiring the focusing visual field of the digital slice and calculating the average definition of all the focusing visual fields;
the kernel calculation module is used for calculating the kernel of the current view image;
the kernel adjusting module is used for carrying out average adjustment on the kernels of the current view images according to the frame views;
the adjustment intensity calculation module is used for calculating the adjustment intensity of the current visual field according to the average adjusted kernel;
and the image adjusting module is used for calculating the image with the adjusted definition according to the adjustment intensity of the current visual field.
As a possible implementation manner of this embodiment, the average sharpness calculating module includes:
the physical slide scanning module is used for scanning a physical slide by using a digital slide scanner to obtain a sample area of a digital slide;
the sample region segmentation module is used for segmenting a sample region of the digital slice to obtain scanning field of view information;
the focusing module is used for selecting a focusing visual field and focusing the focusing visual field;
and the standard deviation module is used for calculating the average definition of all focused visual fields by using a standard deviation method.
As a possible implementation manner of this embodiment, the kernel calculation module includes:
the current definition calculating module is used for calculating the definition of the current visual field image;
the current kernel calculation module is used for calculating the kernel of the current view image:
kCurrentSize＝(1-(viewClarity-focusClarity)/focusClarity)*10
wherein, kCurrentSize is the kernel size of the current view image, viewClarity is the definition of the current view image, focusClarity is the average definition of all focused views;
and an odd number taking module for taking an odd number of the kCurrentSize to calculate:
kCurrentSize＝ceil(kCurrentSize)/2*2+1。
as a possible implementation manner of this embodiment, the kernel adjusting module includes:
the upper view judgment module is used for judging whether the upper view exists in the current view, if so, taking out the kernel of the current view, and carrying out average adjustment on the kernel of the current view;
the lower view judgment module is used for judging whether the current view has a lower view, if so, taking out the kernel of the current view and carrying out average adjustment on the kernel of the current view;
the left view judging module is used for judging whether the current view has a left view, if so, taking out the kernel of the current view and carrying out average adjustment on the kernel of the current view;
the formula for carrying out average adjustment on the current visual field kernel is as follows:
kCurrentSize＝kCurrentSize*(iNum/(iNum+1))+EdgeSize(1.0/(iNum+1.0))
wherein EdgeSize is the kernel in the top or bottom or left view.
As a possible implementation manner of this embodiment, the adjustment strength calculation module is specifically configured to:
calculating the odd number of the kCurrentSize;
calculating the adjustment intensity of the current visual field:
dCurrentDepth＝kCurrentSize*0.09；
wherein dCurrentDepth is the adjustment strength of the current field of view, and kCurrentSize is the kernel size of the current field of view image.
As a possible implementation manner of this embodiment, the image adjusting module includes:
the median filtering module is used for performing median filtering on the original image by utilizing the kernel of the current view image;
the image enhancement module is used for calculating the vision field image after the definition enhancement:
resultCurrentImage＝viewPicture*(2*dCurrentDepth)-blurImage*(2*dCurrentDepth-1)；
wherein, the restcurrentimage is the view image after the definition enhancement, the viewPicture is the current view image, the dCurrentDepth is the adjustment intensity of the current view, and the blurrimage is the view image after the median filtering.
The process of dynamically adjusting the picture definition of the digital slice scanner by adopting the device of the invention is as follows.
Step 1: putting the physical slide into a digital slide scanner, and obtaining a sample area by using a preview camera; then, dividing the sample region to obtain corresponding scanning view information listScanView; selecting a corresponding focusing visual field, and focusing the focusing visual field; and the average sharpness focusClarity for all focused fields was calculated using the standard deviation method.
Step 2: setting m to be 0; the dictionary size is defined to store the kernel size per view.
2.1, obtaining view information view which needs to be acquired currently, namely listScanView [ m ], wherein m is 0, 1 and 2 … …; and acquiring a current visual field image viewPicture, and calculating image definition viewClarity.
kCurrentSize ═ 10 (1- (viewclay-focusclay)/focusclay); take an odd number for kCurrentSize calculation:
kCurrentSize＝ceil(kCurrentSize)/2*2+1。
iNum＝1。
2.2, judging whether the current visual field has an upper visual field or a lower visual field, if so, taking out the kernel upSize of the current visual field from the dictionary size (if the lower visual field exists, the upSize is down size), and averagely adjusting the current visual field definition kernel:
kCurrentSize＝kCurrentSize*(iNum/(iNum+1))+upSize(1.0/(iNum+1.0))。
the slice scanning process is that odd columns are scanned from top to bottom, even columns are scanned from bottom to top, and the whole slice is scanned from left to right; therefore, only the upper side or the lower side and the left side are searched during searching.
iNum＝iNum+1；
2.3 judging whether the current visual field has a left visual field, if so, taking out the kernel leftSize from the dictionary size, and averagely adjusting the current visual field definition kernel:
kCurrentSize＝kCurrentSize*(iNum/(iNum+1))+LeftSize(1.0/(iNum+1.0))。
2.4 odd-numbered kCurrentSize: kCurrentSize ═ ceil (kCurrentSize)/2 × 2+ 1;
calculating the adjustment intensity of the current visual field: dCurrentDepth ═ kCurrentSize ═ 0.09.
Performing median filtering on the original image by using the kCurrentSize to obtain blurImage; obtaining a sharpness-enhanced image: resultCurrentImage (2 × jurrentdepth) -blurrimage (2 × jurrentdepth-1);
2.5 Add the current view's adjustment kernel dCurrentSize to the DictionarySize.
Registering and fusing the resultCurrentimage to form a tile map and storing the tile map;
2.6m ═ m +1, if m is less than the number of scan fields listScanView, go to step 2.1; otherwise, turning to the step 3;
and step 3: and storing all information of the digital slice into a file, and finishing scanning, thereby finishing the dynamic adjustment of the definition of the picture. The image definition can be subjected to convolution operation through a Laplace mask, and the standard deviation is calculated to obtain a value representing the image definition.
The sharpening method adopted by the invention is as follows:
median filtering: blur (srcImage, dstImage, SizekSize, pointeAnchor ═ Point (-1, -1))
The srcImage is an original image; dstImage is the filtered image; kSize is the size of the kernel and is an odd number; pointAnchor is an anchor point, if the coordinate of the point is negative, the center of the kernel is taken as the anchor point.
Sharpness enhanced image: resultImage (srcmmage (2) × dddepth) -dstImage (2) × dddepth-1);
dDepth: to adjust the intensity.
In this sharpness enhancement algorithm, the key roles are kSize and dDepth, both values being the larger the enhancement; the formula dDepth ═ kSize 0.09 is set.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method for dynamically adjusting the image definition of a digital slice scanner is characterized by comprising the following steps:
acquiring the focusing visual field of the digital slice, and calculating the average definition of all the focusing visual fields;
calculating a kernel of the current view image;
carrying out average adjustment on the kernel of the current view image according to the frame view;
calculating the adjustment intensity of the current visual field according to the average adjusted kernel;
and calculating the image with the adjusted definition according to the adjustment intensity of the current visual field.
2. The method of claim 1, wherein said obtaining a focused field of view of the digital slice and calculating an average sharpness for all focused fields of view comprises:
scanning the physical slide by using a digital slide scanner to obtain a sample area of the digital slide;
dividing a sample region of the digital slice to obtain scanning field of view information;
selecting a focusing visual field, and focusing the focusing visual field;
the mean sharpness for all focused views was calculated using the standard deviation method.
3. The method of claim 2, wherein said calculating a kernel of the current view image comprises:
calculating the definition of the current view image;
calculating a kernel of the current view image:
kCurrentSize＝(1-(viewClarity-focusClarity)/focusClarity)*10
wherein, kCurrentSize is the kernel size of the current view image, viewClarity is the definition of the current view image, focusClarity is the average definition of all focused views;
an odd number is calculated for kCurrentSize, kCurrentSize ═ ceil (kCurrentSize)/2 × 2+ 1.
4. The method of claim 3, wherein the performing an average adjustment of the kernel of the current view image based on the bounding box view comprises:
judging whether the current view has an upper view, if so, taking out a kernel of the current view, and carrying out average adjustment on the kernel of the current view;
judging whether the current view has a lower view, if so, taking out a kernel of the current view, and carrying out average adjustment on the kernel of the current view;
and judging whether the current view has a left view, if so, taking out the kernel of the current view, and carrying out average adjustment on the kernel of the current view.
5. The method of claim 4, wherein said calculating the adjusted intensity of the current field of view based on the average adjusted kernel comprises:
calculating the odd number of the kCurrentSize;
calculating the adjustment intensity of the current visual field:
dCurrentDepth＝kCurrentSize*0.09；
wherein dCurrentDepth is the adjustment strength of the current field of view, and kCurrentSize is the kernel size of the current field of view image.
6. The method for dynamically adjusting picture sharpness of a digital slice scanner according to claim 5, wherein said calculating a sharpness-adjusted image based on the adjusted intensity of the current field of view comprises:
performing median filtering on the original image by using the kernel of the current view image;
calculating a visual field image after sharpness enhancement:
resultCurrentImage＝viewPicture*(2*dCurrentDepth)-blurImage*(2*dCurrentDepth-1)；
wherein, the restcurrentimage is the view image after the definition enhancement, the viewPicture is the current view image, the dCurrentDepth is the adjustment intensity of the current view, and the blurrimage is the view image after the median filtering.
7. The utility model provides a device of picture definition dynamic adjustment of digital slice scanner which characterized by includes:
the average definition calculation module is used for acquiring the focusing visual field of the digital slice and calculating the average definition of all the focusing visual fields;
the kernel calculation module is used for calculating the kernel of the current view image;
the kernel adjusting module is used for carrying out average adjustment on the kernels of the current view images according to the frame views;
the adjustment intensity calculation module is used for calculating the adjustment intensity of the current visual field according to the average adjusted kernel;
and the image adjusting module is used for calculating the image with the adjusted definition according to the adjustment intensity of the current visual field.
8. The apparatus for dynamically adjusting picture sharpness of a digital slice scanner of claim 7, wherein the average sharpness calculation module comprises:
the physical slide scanning module is used for scanning a physical slide by using a digital slide scanner to obtain a sample area of a digital slide;
the sample region segmentation module is used for segmenting a sample region of the digital slice to obtain scanning field of view information;
the focusing module is used for selecting a focusing visual field and focusing the focusing visual field;
and the standard deviation module is used for calculating the average definition of all focused visual fields by using a standard deviation method.
9. The apparatus for dynamically adjusting sharpness of a picture in a digital slice scanner of claim 7, wherein the kernel computing module comprises:
the current definition calculating module is used for calculating the definition of the current visual field image;
the current kernel calculation module is used for calculating the kernel of the current view image:
kCurrentSize＝(1-(viewClarity-focusClarity)/focusClarity)*10
wherein, kCurrentSize is the kernel size of the current view image, viewClarity is the definition of the current view image, focusClarity is the average definition of all focused views;
and an odd number taking module for taking an odd number of the kCurrentSize to calculate:
kCurrentSize＝ceil(kCurrentSize)/2*2+1。
10. the apparatus for dynamically adjusting sharpness of a picture in a digital slice scanner of claim 7, wherein the image adjustment module comprises:
the median filtering module is used for performing median filtering on the original image by utilizing the kernel of the current view image;
the image enhancement module is used for calculating the vision field image after the definition enhancement:
resultCurrentImage＝viewPicture*(2*dCurrentDepth)-blurImage*(2*dCurrentDepth-1)；
wherein, the restcurrentimage is the view image after the definition enhancement, the viewPicture is the current view image, the dCurrentDepth is the adjustment intensity of the current view, and the blurrimage is the view image after the median filtering.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110781905.7A CN113487508B (en) | 2021-07-08 | 2021-07-08 | Method and device for dynamically adjusting picture definition of digital slice scanner |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110781905.7A CN113487508B (en) | 2021-07-08 | 2021-07-08 | Method and device for dynamically adjusting picture definition of digital slice scanner |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113487508A true CN113487508A (en) | 2021-10-08 |
| CN113487508B CN113487508B (en) | 2024-03-26 |
Family
ID=77938538
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110781905.7A Active CN113487508B (en) | 2021-07-08 | 2021-07-08 | Method and device for dynamically adjusting picture definition of digital slice scanner |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113487508B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101615289A (en) * | 2009-08-05 | 2009-12-30 | 北京优纳科技有限公司 | The three-dimensional acquisition of biopsy tissues and fusion method of multilayer images |
| JP2011257384A (en) * | 2010-05-10 | 2011-12-22 | Jfe Steel Corp | Method for imaging structure shape in welded part and device therefor |
| CN107507173A (en) * | 2017-08-15 | 2017-12-22 | 上海交通大学 | A kind of full slice image without refer to intelligibility evaluation method and system |
| US20190180420A1 (en) * | 2017-12-13 | 2019-06-13 | Instituto Potosino de Investigación Científica y Tecnológica A.C. | Automated quantitative restoration of bright field microscopy images |
| CN110400281A (en) * | 2019-08-05 | 2019-11-01 | 山东志盈医学科技有限公司 | Image enchancing method in a kind of digital slices scanner |
| CN110992408A (en) * | 2019-11-18 | 2020-04-10 | 迪普派斯医疗科技(山东)有限公司 | Digital section processing method and system based on pathological microscope |
| CN111787233A (en) * | 2020-08-05 | 2020-10-16 | 湖南莱博赛医用机器人有限公司 | Image acquisition method and device and electronic equipment |
| CN112529784A (en) * | 2019-09-18 | 2021-03-19 | 华为技术有限公司 | Image distortion correction method and device |
| CN112697789A (en) * | 2020-12-09 | 2021-04-23 | 山东志盈医学科技有限公司 | Image focusing method and device for digital slice scanner |
-
2021
- 2021-07-08 CN CN202110781905.7A patent/CN113487508B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101615289A (en) * | 2009-08-05 | 2009-12-30 | 北京优纳科技有限公司 | The three-dimensional acquisition of biopsy tissues and fusion method of multilayer images |
| JP2011257384A (en) * | 2010-05-10 | 2011-12-22 | Jfe Steel Corp | Method for imaging structure shape in welded part and device therefor |
| CN107507173A (en) * | 2017-08-15 | 2017-12-22 | 上海交通大学 | A kind of full slice image without refer to intelligibility evaluation method and system |
| US20190180420A1 (en) * | 2017-12-13 | 2019-06-13 | Instituto Potosino de Investigación Científica y Tecnológica A.C. | Automated quantitative restoration of bright field microscopy images |
| CN110400281A (en) * | 2019-08-05 | 2019-11-01 | 山东志盈医学科技有限公司 | Image enchancing method in a kind of digital slices scanner |
| CN112529784A (en) * | 2019-09-18 | 2021-03-19 | 华为技术有限公司 | Image distortion correction method and device |
| CN110992408A (en) * | 2019-11-18 | 2020-04-10 | 迪普派斯医疗科技(山东)有限公司 | Digital section processing method and system based on pathological microscope |
| CN111787233A (en) * | 2020-08-05 | 2020-10-16 | 湖南莱博赛医用机器人有限公司 | Image acquisition method and device and electronic equipment |
| CN112697789A (en) * | 2020-12-09 | 2021-04-23 | 山东志盈医学科技有限公司 | Image focusing method and device for digital slice scanner |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113487508B (en) | 2024-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8416314B2 (en) | Method and system for processing images | |
| US10168526B2 (en) | Cell contour formation apparatus and method of the same, and non-transitory computer readable storage medium storing a cell contour formation program | |
| EP3358298A1 (en) | Building height calculation method and apparatus, and storage medium | |
| JP6755787B2 (en) | Image processing equipment, image processing methods and programs | |
| CN109064505B (en) | Depth estimation method based on sliding window tensor extraction | |
| JP2005309559A (en) | Image processing method, device and program | |
| WO2013143390A1 (en) | Face calibration method and system, and computer storage medium | |
| CN115047610B (en) | Chromosome karyotype analysis device and method for automatically fitting microscopic focusing plane | |
| WO2017120796A1 (en) | Pavement distress detection method and apparatus, and electronic device | |
| CN106296608B (en) | Mapping table-based fisheye image processing method and system | |
| CN112330613A (en) | Method and system for evaluating quality of cytopathology digital image | |
| CN110400281B (en) | Image enhancement method in digital slice scanner | |
| CN110415251A (en) | A kind of image partition method and device of background detection | |
| WO2018196854A1 (en) | Photographing method, photographing apparatus and mobile terminal | |
| JP2002328096A (en) | Program, method, and system for detecting crack defect generated on structure | |
| CN113487508A (en) | Method and device for dynamically adjusting picture definition of digital slice scanner | |
| JP3860540B2 (en) | Entropy filter and region extraction method using the filter | |
| RU2329535C2 (en) | Method of automatic photograph framing | |
| WO2022127491A1 (en) | Image processing method and device, and storage medium and terminal | |
| CN112734742B (en) | Method and system for improving industrial quality inspection accuracy | |
| CN115578291A (en) | Image brightness correction method, storage medium and electronic device | |
| CN111598940B (en) | Hemispherical photographing center point positioning method | |
| TWI510776B (en) | Bubble inspection processing method for glass | |
| Nagalakshmi et al. | Image acquisition, noise removal, edge detection methods in image processing using Matlab for prawn species identification | |
| WO2020238116A1 (en) | Product positioning method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |