CN107764779B - Super-resolution imaging method and system of confocal system - Google Patents
Super-resolution imaging method and system of confocal system Download PDFInfo
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Abstract
The invention provides a super-resolution imaging method and a super-resolution imaging system of a confocal system, which comprise the following steps: acquiring an original scanning image of a confocal system through an sCMOS camera, and performing cubic interpolation; rearranging the pixel data of the obtained original scanning image to obtain a scanning image after pixel rearrangement; establishing a subtraction imaging coefficient related to the position, and subtracting the pixel point after the pixel rearrangement from the corresponding position of the pixel point after the cubic interpolation to obtain a new image; and carrying out deconvolution processing on the new image to obtain a deconvolved image. The invention can effectively utilize the position information of each pixel, avoid the defect that the traditional method directly adds the images of the scanning points to lose details, and effectively improve the resolution of the confocal system.
Description
Technical Field
The invention relates to the technical field of biophoton microscopic imaging, in particular to a super-resolution imaging method and a super-resolution imaging system of a confocal system.
Background
In the industrial and biological fields, laser scanning microscopes can be divided into two types, i.e., a reflective confocal system and a fluorescent confocal system. Many methods have been proposed to break the optical diffraction limit, such as the stimulated emission depletion (STED) method, which can change the effective point spread function of the excitation beam by suppressing the fluorescence emission of fluorophores far away from the excitation center using a second laser beam. Random optical reconstruction microscopy (STORM) and PALM microscopy focus on the area where the fluorescent molecules are located, and structured light microscopy (SIM) converts the information of high spatial frequency into direct current frequency conversion, so that it can pass through a low-pass filter. Image scanning microscopy has been proposed by Muller et al and provides a theoretical basis for replacing the physical pinhole and detector with a CCD capable of recording positional information. However, the conventional super-resolution imaging method of the confocal system directly adds the images of the scanning points, thereby bringing the disadvantage of losing details.
Disclosure of Invention
In view of the defects in the prior art, the present invention provides a super-resolution imaging method and system of a confocal system.
The invention provides a super-resolution imaging method of a confocal system, which comprises the following steps
An image acquisition step: acquiring an original scanning image of a confocal system through an sCMOS camera, and performing cubic interpolation;
a pixel rearrangement step: rearranging the pixel data of the obtained original scanning image to obtain a scanning image after pixel rearrangement;
a subtraction imaging coefficient establishing step: establishing a position-related subtraction imaging coefficient, and subtracting the corresponding positions of the scanned image after pixel rearrangement and the scanned image after cubic interpolation to obtain a new image;
and (3) deconvolution processing: and carrying out deconvolution processing on the new image to obtain a deconvolved image.
Preferably, the cubic interpolation is linear interpolation, and the interpolation point is the midpoint of the original adjacent pixel point.
Preferably, the original scan image formed at the k scan points has a value of (i, j) at position (i, j)
Has a value of (i +1, j) adjacent to the pixel point of (b)
The pixel point of (2) is an interpolation point
The value of the pixel at is
Preferably, the rearranging the pixel data includes:
the pixels are close to the center of the excitation light with the center of the excitation light (u, v) as the central axis, and the coordinate is (i)1,j1) Is assigned to the coordinate (i)2,j2) And then:
preferably, the establishing the position-dependent subtraction imaging coefficient includes:
having a value of I at position (I, j) on the scanned image after cubic interpolation formed at k scanning points2The pixel point of (I, j) has a value of I at the position (I, j) after pixel redistribution1(i, j) pixel points are subtracted
Isub(i,j)＝I1(i,j)-k(I2(i,j)-I1(i,j))k≥0
The value of the coefficient k ensures the nonnegativity of the image pixels imaged by the subtraction method and the integrity of information.
According to the present invention, a super-resolution imaging system of a confocal system comprises:
an image acquisition module: acquiring an original scanning image of a confocal system through an sCMOS camera, and performing cubic interpolation;
a pixel rearrangement module: rearranging the pixel data of the obtained original scanning image to obtain a scanning image after pixel rearrangement;
a subtraction imaging coefficient establishing module: establishing a position-related subtraction imaging coefficient, and subtracting the corresponding positions of the scanned image after pixel rearrangement and the original scanned image to obtain a new image;
a deconvolution processing module: and carrying out deconvolution processing on the new image to obtain a deconvolved image.
Preferably, the cubic interpolation is linear interpolation, and the interpolation point is the midpoint of the original adjacent pixel point.
Preferably, the original scan image formed at the k scan points has a value of (i, j) at position (i, j)
Has a value of (i +1, j) adjacent to the pixel point of (b)
The pixel point of (2) is an interpolation point
The value of the pixel at is
Preferably, the rearranging the pixel data includes:
the pixels are close to the center of the excitation light with the center of the excitation light (u, v) as the central axisTogether, the coordinates are (i)1,j1) Is assigned to the coordinate (i)2,j2) And then:
preferably, the establishing the position-dependent subtraction imaging coefficient includes:
having a value of I at position (I, j) on the scanned image after cubic interpolation formed at k scanning points2The pixel point of (I, j) has a value of I at the position (I, j) after pixel redistribution1(i, j) pixel points are subtracted
Isub(i,j)＝I1(i,j)-k(I2(i,j)-I1(i,j))k≥0
The value of the coefficient k ensures the nonnegativity of the image pixels imaged by the subtraction method and the integrity of information.
Compared with the prior art, the invention has the following beneficial effects:
the invention can effectively utilize the position information of each pixel, avoid the defect that the traditional method directly adds the images of the scanning points to lose details, and effectively improve the resolution of the confocal system.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of confocal super-resolution imaging according to the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in FIG. 1, the present invention provides a super-resolution imaging method of confocal system, which comprises
An image acquisition step: acquiring an original scanning image of a confocal system through an sCMOS camera, and performing cubic interpolation;
a pixel rearrangement step: rearranging the pixel data of the obtained original scanning image through a pixel redistribution theory to obtain a scanning image after pixel rearrangement;
a subtraction imaging coefficient establishing step: establishing a position-related subtraction imaging coefficient, and subtracting the corresponding positions of the scanned image after pixel rearrangement and the original scanned image to obtain a new image;
and (3) deconvolution processing: and carrying out deconvolution processing on the new image to obtain a deconvolved image.
Assuming that N points are scanned in total, the number of pixels imaged on sCMOS by each scanning point is N1×N2Then N × N is obtained altogether1×N2Value of each pixel
The cubic interpolation is linear interpolation, and the interpolation point is the midpoint of the original adjacent pixel point. Specifically, the original scan image formed at the k scan points has a value of (i, j) at the position
Has a value of (i +1, j) adjacent to the pixel point of (b)
The pixel point of (2) is an interpolation point
The value of the pixel at is
Rearranging the pixel data includes:
with the center (u, v) of the exciting light as the central axis, and othersThe pixel is close to the center of the exciting light and has the coordinate of (i)1,j1) Is assigned to the coordinate (i)2,j2) And then:
establishing the position-dependent subtraction imaging coefficients comprises:
having a value of I at position (I, j) on the scanned image after cubic interpolation formed at k scanning points2The pixel point of (I, j) has a value of I at the position (I, j) after pixel redistribution1(i, j) pixel points are subtracted
Isub(i,j)＝I1(i,j)-k(I2(i,j)-I1(i,j))k≥0
The value of the coefficient k ensures the nonnegativity of the image pixels imaged by the subtraction method and the integrity of information.
The deconvolution process includes:
assuming that K is the point spread function measured by calibration, ItIs the image of the t-th iteration. Specifically, deconvolution was performed using the Richardson-lucy algorithm:
on the basis of the above super-resolution imaging method of the confocal system, the present invention further provides a super-resolution imaging system of the confocal system, which includes:
an image acquisition module: acquiring an original scanning image of a confocal system through an sCMOS camera, and performing cubic interpolation;
a pixel rearrangement module: rearranging the pixel data of the obtained original scanning image through a pixel redistribution theory to obtain a scanning image after pixel rearrangement;
a subtraction imaging coefficient establishing module: establishing a position-related subtraction imaging coefficient, and subtracting the corresponding positions of the scanned image after pixel rearrangement and the original scanned image to obtain a new image;
a deconvolution processing module: and carrying out deconvolution processing on the new image to obtain a deconvolved image.
The cubic interpolation is linear interpolation, and the interpolation point is the midpoint of the original adjacent pixel point. Specifically, the original scan image formed at the k scan points has a value of (i, j) at the position
Has a value of (i +1, j) adjacent to the pixel point of (b)
The pixel point of (2) is an interpolation point
The value of the pixel at is
Rearranging the pixel data includes:
the pixels are close to the center of the excitation light with the center of the excitation light (u, v) as the central axis, and the coordinate is (i)1,j1) Is assigned to the coordinate (i)2,j2) And then:
establishing the position-dependent subtraction imaging coefficients comprises:
having a value of I at position (I, j) on the scanned image after cubic interpolation formed at k scanning points2The pixel point of (I, j) has a value of I at the position (I, j) after pixel redistribution1(i, j) pixel points are subtracted
Isub(i,j)＝I1(i,j)-k(I2(i,j)-I1(i,j))k≥0
The value of the coefficient k ensures the nonnegativity of the image pixels imaged by the subtraction method and the integrity of information.
The deconvolution process includes:
hypothesis K is measured by calibrationPoint spread function, ItIs the image of the t-th iteration. Specifically, deconvolution was performed using the Richardson-lucy algorithm:
those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (2)
1. A super-resolution imaging method of a confocal system is characterized by comprising
An image acquisition step: acquiring an original scanning image of a confocal system through an sCMOS camera, and performing cubic interpolation;
a pixel rearrangement step: rearranging the pixel data of the obtained original scanning image to obtain a scanning image after pixel rearrangement;
a subtraction imaging coefficient establishing step: establishing a position-related subtraction imaging coefficient, and subtracting the corresponding positions of the scanned image after pixel rearrangement and the original scanned image after cubic interpolation to obtain a new image;
and (3) deconvolution processing: carrying out deconvolution processing on the new image to obtain a deconvolved image;
the cubic interpolation is linear interpolation, and the interpolation point is the midpoint of the original adjacent pixel point;
has a value of (i, j) at a position on the original scan image formed at the k scan points
Has a value of (i +1, j) adjacent to the pixel point of (b)
The pixel point of (2) is an interpolation point
The value of the pixel at is
Rearranging the pixel data includes:
the pixels are close to the center of the excitation light with the center of the excitation light (u, v) as the central axis, and the coordinate is (i)1,j1) Is assigned to the coordinate (i)2,j2) And then:
the establishing of the position-dependent subtraction imaging coefficients comprises:
having a value of I at position (I, j) on the scanned image after cubic interpolation formed at k scanning points2(I, j) of the pixel point, the position (I, j) has a value of I after the pixel rearrangement1Subtracting the pixel point of (i, j) from the pixel point after cubic interpolation
Isub(i,j)＝I1(i,j)-k(I2(i,j)-I1(i,j))k≥0
The value of the coefficient k ensures the nonnegativity of the image pixels imaged by the subtraction method and the integrity of the information;
the deconvolution process includes:
assuming that K is the point spread function measured by calibration, ItIs the image of the t iteration; specifically, deconvolution was performed using the Richardson-lucy algorithm:
2. a confocal system super-resolution imaging system, comprising:
an image acquisition module: acquiring an original scanning image of a confocal system through an sCMOS camera, and performing cubic interpolation;
a pixel rearrangement module: rearranging the pixel data of the obtained original scanning image to obtain a scanning image after pixel rearrangement;
a subtraction imaging coefficient establishing module: establishing a position-related subtraction imaging coefficient, and subtracting the corresponding positions of the scanned image after pixel rearrangement and the original scanned image after cubic interpolation to obtain a new image;
a deconvolution processing module: carrying out deconvolution processing on the new image to obtain a deconvolved image;
the cubic interpolation is linear interpolation, and the interpolation point is the midpoint of the original adjacent pixel point;
has a value of (i, j) at a position on the original scan image formed at the k scan points
Has a value of (i +1, j) adjacent to the pixel point of (b)
The pixel point of (2) is an interpolation point
The value of the pixel at is
Rearranging the pixel data includes:
the pixels are close to the center of the excitation light with the center of the excitation light (u, v) as the central axis, and the coordinate is (i)1,j1) Is assigned to the coordinate (i)2,j2) And then:
the establishing of the position-dependent subtraction imaging coefficients comprises:
having a value of I at position (I, j) on the scanned image after cubic interpolation formed at k scanning points2(I, j) of the pixel point, the position (I, j) has a value of I after the pixel rearrangement1Subtracting the pixel point of (i, j) from the pixel point after cubic interpolation
Isub(i,j)＝I1(i,j)-k(I2(i,j)-I1(i,j))k≥0
The value of the coefficient k ensures the nonnegativity of the image pixels imaged by the subtraction method and the integrity of the information;
the deconvolution process includes:
assuming that K is the point spread function measured by calibration, ItIs the image of the t iteration; specifically, deconvolution was performed using the Richardson-lucy algorithm:
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| CN109557653B (en) * | 2018-12-20 | 2021-06-29 | 浙江大学 | Differential confocal microscopic imaging method and device based on algorithm recovery |
| CN111257226B (en) * | 2020-01-18 | 2022-10-28 | 南京恒锐精密仪器有限公司 | Dark field confocal microscopic measurement device and method based on polarization autocorrelation |
| CN111257227B (en) * | 2020-01-18 | 2023-01-31 | 南京恒锐精密仪器有限公司 | Dark field confocal microscopic measurement device and method based on polarization autocorrelation |
| CN112653834B (en) * | 2020-12-01 | 2022-04-08 | 广东鼎诚电子科技有限公司 | Super-resolution scanning imaging method, system and storage medium |
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6160584A (en) * | 1997-05-21 | 2000-12-12 | Sony Corporation | Motion detection and motion compensative prediction circuit |
| CN1901602A (en) * | 2006-07-10 | 2007-01-24 | 宁波大学 | Quick double linear interpolating method in image amplification process |
| US7262892B1 (en) * | 2006-06-19 | 2007-08-28 | Fuji Xerox Co., Ltd. | Hologram reconstruction method and device |
| CN101521741A (en) * | 2009-04-08 | 2009-09-02 | 中国科学院长春光学精密机械与物理研究所 | Digital correlation dual-sampling circuit of CCD picture sensor |
| CN101777193A (en) * | 2010-02-03 | 2010-07-14 | 中国人民解放军国防科学技术大学 | Bidirectional mapping precise interpolation-based cylindrical panoramic expansion method for refractive-reflective all-directional picture |
| CN102800047A (en) * | 2012-06-20 | 2012-11-28 | 天津工业大学 | Method for reconstructing super resolution of single-frame image |
| CN103268599A (en) * | 2013-04-19 | 2013-08-28 | 中国科学院长春光学精密机械与物理研究所 | Multi-linear-array charge coupled device (CCD) sub-pixel staggered imaging super-resolution reconstruction method |
| CN103903235A (en) * | 2014-01-27 | 2014-07-02 | 西安理工大学 | Cell image super-resolution reconstruction method |
| CN103929570A (en) * | 2013-01-16 | 2014-07-16 | 富士通株式会社 | Image processing method and system |
| JP5779967B2 (en) * | 2011-05-12 | 2015-09-16 | 株式会社リコー | Optical writing apparatus, image forming apparatus, and correction value information generation method |
| CN106327437A (en) * | 2016-08-10 | 2017-01-11 | 大连海事大学 | Color text image correction method and system |
| CN107144551A (en) * | 2017-04-12 | 2017-09-08 | 上海戴泽光电科技有限公司 | The burnt super-resolution imaging system and method for copolymerization based on sCMOS |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9459201B2 (en) * | 2014-09-29 | 2016-10-04 | Zyomed Corp. | Systems and methods for noninvasive blood glucose and other analyte detection and measurement using collision computing |
| US9722758B2 (en) * | 2015-05-20 | 2017-08-01 | Hong Kong Applied Science and Technology Research Institute Company Limited | Channel-quality estimation for a wireless channel |
-
2017
- 2017-09-27 CN CN201710891789.8A patent/CN107764779B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6160584A (en) * | 1997-05-21 | 2000-12-12 | Sony Corporation | Motion detection and motion compensative prediction circuit |
| US7262892B1 (en) * | 2006-06-19 | 2007-08-28 | Fuji Xerox Co., Ltd. | Hologram reconstruction method and device |
| CN1901602A (en) * | 2006-07-10 | 2007-01-24 | 宁波大学 | Quick double linear interpolating method in image amplification process |
| CN101521741A (en) * | 2009-04-08 | 2009-09-02 | 中国科学院长春光学精密机械与物理研究所 | Digital correlation dual-sampling circuit of CCD picture sensor |
| CN101777193A (en) * | 2010-02-03 | 2010-07-14 | 中国人民解放军国防科学技术大学 | Bidirectional mapping precise interpolation-based cylindrical panoramic expansion method for refractive-reflective all-directional picture |
| JP5779967B2 (en) * | 2011-05-12 | 2015-09-16 | 株式会社リコー | Optical writing apparatus, image forming apparatus, and correction value information generation method |
| CN102800047A (en) * | 2012-06-20 | 2012-11-28 | 天津工业大学 | Method for reconstructing super resolution of single-frame image |
| CN103929570A (en) * | 2013-01-16 | 2014-07-16 | 富士通株式会社 | Image processing method and system |
| CN103268599A (en) * | 2013-04-19 | 2013-08-28 | 中国科学院长春光学精密机械与物理研究所 | Multi-linear-array charge coupled device (CCD) sub-pixel staggered imaging super-resolution reconstruction method |
| CN103903235A (en) * | 2014-01-27 | 2014-07-02 | 西安理工大学 | Cell image super-resolution reconstruction method |
| CN106327437A (en) * | 2016-08-10 | 2017-01-11 | 大连海事大学 | Color text image correction method and system |
| CN107144551A (en) * | 2017-04-12 | 2017-09-08 | 上海戴泽光电科技有限公司 | The burnt super-resolution imaging system and method for copolymerization based on sCMOS |
Non-Patent Citations (9)
| Title |
|---|
| Channel correlated refinement for color interpolation with quincuncial patterns containing the white channel;Sang Wook Park;《Digital Signal Processing》;20130930;第23卷(第5期);全文 * |
| Compact image capturing system based on compound imaging and digital reconstruction;Jun Tanida;《Proceedings of Spie the International Society》;20010731;全文 * |
| Image interpolation using across-scale pixel correlation;Tao Chen;《IEEE International Conference on Acoustics, Speech, and Signal Processing》;20010511;全文 * |
| Moving object recognition under simulated prosthetic vision;Jing Wang;《Information Sciences》;20140930;第277卷;全文 * |
| 低对比度显微图像的颜色增强方法研究;朱美娇;《中国优秀硕士学位论文全文数据库信息科技辑》;20140930(第9期);全文 * |
| 基于亚像素位移的超分辨率图像重建算法;张东晓;《自动化学报》;20141231;第40卷(第12期);全文 * |
| 用双层重建法实现单幅图像的超分辨率重建;龚卫国;《光学精密工程》;20140331;第22卷(第3期);全文 * |
| 视觉修复系统模型建立与信息处理;夏鹏;《上海交通大学学报》;20140228;第48卷(第2期);全文 * |
| 超分辨率图像重建算法研究;侯鲜桃;《中国优秀硕士学位论文全文数据库信息科技辑》;20120515(第5期);全文 * |
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