CN116402917A - Method for determining image to be reconstructed by wide-spectrum optical speckle autocorrelation imaging - Google Patents
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Abstract
The application provides a method for determining an image to be reconstructed of wide-spectrum optical speckle autocorrelation imaging, which comprises the following steps: acquiring a sampling image of wide-spectrum optical speckle autocorrelation imaging; determining a primary screening size according to the sampling image, wherein the primary screening size is smaller than the size of the sampling image; determining a gradient sum of a plurality of prescreened pixel arrays having a prescreening size in the sampled image; acquiring a gradient and a central pixel of the central position of the maximum primary screening pixel array; acquiring a plurality of sub-sampling images with a plurality of different sub-sampling sizes by taking a central position corresponding to a central pixel as an image central position; if the peak background ratio of the minimum sub-sampling size in the plurality of different sub-sampling sizes is maximum, the primary screening size is redetermined; if the peak background ratio of the minimum sub-sampling size is not the maximum, determining the sub-sampling image with the maximum peak background ratio as an image to be reconstructed, and using the image to be reconstructed. Therefore, the image to be reconstructed, which is close to the optical axis and has proper size, can be obtained quickly, and the self-correlation imaging capability of the wide-spectrum optical speckle is improved.
Description
Technical Field
The application relates to the technical field of optical imaging and image processing, in particular to a method for determining an image to be reconstructed by broad spectrum optical speckle autocorrelation imaging.
Background
Due to the scattering characteristics of scattering media such as fog, haze, clouds and biological tissues in the nature, the emergent field is disturbed when light passes through the scattering media, so that an image obtained by a traditional imaging method is blurred and even fails.
The novel imaging methods can effectively solve the problems, and have great application advantages in the fields of biological imaging, underwater detection, low-visibility environment imaging and the like. The more common imaging methods include: the method comprises the steps of utilizing a time of flight or a time/coherence gating method of coherence of ballistic light, utilizing an iterative wave front shaping method of phase circulation optimization, utilizing a matrix measuring method of an advanced calibration system and matrix operation, utilizing a deconvolution method of a point spread function, utilizing a speckle autocorrelation imaging method of a memory effect and a phase recovery algorithm, and the like.
The gating method is only suitable for a relatively thin scattering medium to ensure the existence of ballistic light, and the iterative wave-front shaping method, the matrix measuring method and the deconvolution method need to invasively place a reference object on one side of a target object, so that the application prospect is greatly limited.
The speckle autocorrelation imaging method firstly utilizes autocorrelation calculation and Wei Naxin k theorem to extract an image to be reconstructed of a target object from a sampling image, and then reconstructs the image of the target object by means of a phase recovery algorithm. The method does not need an advanced measurement system, has lower requirement on system stability, gradually becomes a research hot spot in recent years, and obviously improves resolution, imaging speed and view angle.
The speckle autocorrelation imaging method can reconstruct an image of a target object in a non-invasive way, but is more suitable for narrow-band incoherent light imaging, and for a wide-spectrum light imaging scene with a certain bandwidth of illumination wavelength, excessive statistical noise can easily cause image reconstruction failure.
The imaging bandwidth can be limited and adjusted by placing a narrow-band filter in the optical path, but the signal strength can be greatly reduced, the signal-to-noise ratio is reduced, and even the signal is completely annihilated by noise fluctuation. When the illumination bandwidth is within a limited range (generally not more than 50 nm), effective information for image reconstruction is not completely confused and lost, at this time, speckle images corresponding to each wavelength in the bandwidth have relatively large similarity near an optical axis, and a sampling image near the optical axis is consistent with a speckle image corresponding to a single wavelength in height, so that the method can still be used for extracting images to be reconstructed of a target object.
There are two key issues with image reconstruction: firstly, determining the optical axis of a system under the non-invasive and non-priori condition or directly selecting an image to be reconstructed, which is close to the optical axis, from the sampled images; and secondly, selecting an image to be reconstructed with a proper size for autocorrelation calculation so as to avoid insufficient effective sample number or excessive interference information caused by too small or too large image.
While the above two problems can be solved by repeatedly attempting to reconstruct in a loop-through fashion, it is not feasible to waste too much computation and time resources. Therefore, it is needed to provide an imaging method capable of rapidly determining an ideal image to be reconstructed for image reconstruction, optimizing imaging speed and quality, and improving imaging capability of the speckle autocorrelation technology under wide spectrum illumination, so as to greatly improve universality of the speckle autocorrelation imaging method, and make the speckle autocorrelation imaging method easier to be applied to actual natural illumination scenes.
Disclosure of Invention
The method for determining the image to be reconstructed of the wide-spectrum optical speckle autocorrelation imaging can obtain the image to be reconstructed which is suitable in size and is close to the optical axis.
The application provides a method for determining an image to be reconstructed of wide-spectrum optical speckle autocorrelation imaging, which comprises the following steps:
Acquiring a sampling image of the wide spectrum optical speckle autocorrelation imaging;
determining a primary screening size according to the sampling image, wherein the primary screening size is smaller than the size of the sampling image;
determining a sum of gradients of a plurality of prescreened pixel arrays having the prescreening size in the sampled image;
acquiring a gradient and a maximum central pixel of the central position of the primary screening pixel array;
acquiring a plurality of sub-sampling images with a plurality of different sub-sampling sizes by taking the central position corresponding to the central pixel as an image central position;
if the peak background ratio of the minimum sub-sampling size in the plurality of different sub-sampling sizes is maximum, the primary screening size is redetermined;
and if the peak background ratio of the minimum sub-sampling size is not the maximum, determining the sub-sampling image with the maximum peak background ratio as the image to be reconstructed, and using the image to be reconstructed.
Optionally, the sequence of the pixel array of the sampled image includes a first sequence and a second sequence, one of the first sequence and the second sequence is a row, and the other is a column; the determining the primary screening size according to the sampling image comprises the following steps:
obtaining a plurality of pixels of a first sequence from the pixel array of the sampling image at intervals of a first set sequence number in the direction of the second sequence;
Generating a graph of light intensity data for each of the plurality of first sequence pixels with the number of sequences of the second sequence;
acquiring the full width at half maximum of the graph of each first sequence of pixels;
determining the primary screen size according to the number of sequences of the second sequence in which the full width at half maximum of the plots of pixels of the plurality of first sequences most coincides;
wherein said determining said primary screen size from said number of sequences of said second sequence for which said full width at half maximum of said plot of pixels of said plurality of first sequences most coincides comprises:
and determining half of the width of the sequence number of the second sequence with the largest half width overlapping as the primary screening size.
Optionally, the sub-sampling size comprises one-half of the primary screen size, two times the primary screen size, and a size between one-half of the primary screen size and two times the primary screen size.
Optionally, if the peak background ratio of the smallest sub-sampling size of the plurality of different sub-sampling sizes is the largest, re-determining the primary screening size includes:
obtaining a plurality of pixels of a first sequence from the pixel array of the sampled image at intervals of a second set sequence number in the direction of the second sequence, wherein the second set sequence number is smaller than the first set sequence number;
Generating a graph of light intensity data for each first sequence of pixels of the plurality of first sequence pixels with a sequence number of the second sequence;
acquiring the full width at half maximum of the graph of each first sequence of pixels;
and determining the size of the primary screen according to the sequence number of the second sequence with the maximum half-width coincidence of the curve graph of the pixels of the plurality of first sequences.
Optionally, the first sequence is rows, the second sequence is columns, and determining a primary screening size according to the sampled image includes:
acquiring a plurality of columns of pixels from the pixel array of the sampling image at intervals of a first set column number;
generating a graph of light intensity data and pixel row numbers for each column of pixels of the plurality of columns of pixels;
acquiring the full width at half maximum of the graph of each column of pixels;
determining the primary screening size according to the pixel row number with the maximum half-width coincidence of the curve graph of the multi-column pixels;
wherein said determining said prescreen size based on said number of rows of pixels with said full width at half maximum coincidence of said plots of said plurality of columns of pixels comprises:
and determining half of the width of the pixel row number with the maximum half width overlapping as the primary screening size.
Optionally, if the peak background ratio of the smallest sub-sampling size of the plurality of different sub-sampling sizes is the largest, re-determining the primary screening size includes:
obtaining a plurality of columns of pixels from the pixel array of the sampling image at intervals of a second set column number, wherein the second set column number is smaller than the first set column number;
generating a graph of light intensity data and pixel row numbers for each column of pixels of the plurality of columns of pixels;
acquiring the full width at half maximum of the graph of each column of pixels;
and determining the primary screening size according to the pixel row number with the maximum half-width coincidence of the curve graph of the pixel columns.
Optionally, the first sequence is rows, the second sequence is columns, and determining a primary screening size according to the sampled image includes:
acquiring a plurality of rows of pixels from the pixel array of the sampling image at intervals of a first set row number;
generating a graph of light intensity data and pixel column numbers of each row of pixels of the plurality of rows of pixels;
acquiring the full width at half maximum of the graph of each row of pixels;
determining the primary screening size according to the pixel column number with the maximum half-width coincidence of the curve graph of the plurality of rows of pixels;
Wherein said determining said prescreen size based on said number of columns of pixels with the largest overlap of said full width at half maximum of said plot of said plurality of rows of pixels comprises:
and determining half of the width of the pixel column number with the maximum half width overlapping as the primary screening size.
Optionally, if the peak background ratio of the smallest sub-sampling size of the plurality of different sub-sampling sizes is the largest, re-determining the primary screening size includes:
acquiring a plurality of rows of pixels from the pixel array of the sampling image at intervals of a second set row number, wherein the second set row number is smaller than the first set row number;
generating a graph of light intensity data and pixel column numbers of each row of pixels of the plurality of rows of pixels;
acquiring the full width at half maximum of the graph of each row of pixels;
and determining the primary screening size according to the pixel column number with the maximum half-width coincidence of the curve graph of the plurality of rows of pixels.
Optionally, the determining a gradient sum of a plurality of prescreened pixel arrays having the prescreening size in the sampled image includes:
traversing the pixel arrays of the sampled images according to the primary screening size to obtain a plurality of primary screening pixel arrays;
Determining a sum of gradients of the plurality of arrays of primary screen pixels, respectively; or (b)
The determining a gradient sum of a plurality of arrays of prescreened pixels in the sampled image having the prescreened size comprises:
selecting the plurality of prescreened pixel arrays according to the prescreening size in the pixel arrays of the sampled image with at least one row and/or at least one column spacing;
the gradient sums of the plurality of arrays of primary screen pixels are determined separately.
Optionally, if the peak background ratio of the minimum sub-sampling size is not the maximum, determining the sub-sampling image with the maximum peak background ratio as the image to be reconstructed includes:
respectively carrying out autocorrelation operation on the plurality of sub-sampling images; and
And respectively calculating peak background ratios of the sub-sampling images.
In some embodiments, the preliminary screening size is determined according to the sampled image, a plurality of preliminary screening pixel arrays are determined in the sampled image, and central pixels of gradients and central positions of the largest preliminary screening pixel arrays are acquired, so that the central positions of the images to be reconstructed close to the optical axis can be screened out faster, sub-sampled images with different sub-sampling sizes taking the central positions corresponding to the central pixels as the central positions of the images are acquired, and if the peak background ratio of the smallest sub-sampling size is not the largest, the sub-sampled image with the largest peak background ratio is determined as the images to be reconstructed, and the images to be reconstructed with proper size can be obtained. Therefore, the image to be reconstructed, which is close to the optical axis and has proper size, can be obtained quickly, and the self-correlation imaging capability of the wide-spectrum optical speckle is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flow chart illustrating one embodiment of a method of determining an image to be reconstructed for broad spectrum optical speckle auto-correlation imaging of the present application.
Fig. 2 is a schematic diagram of broad spectrum optical speckle auto-correlation imaging.
FIG. 3 is a flow chart illustrating one embodiment of step 12 shown in FIG. 1.
Fig. 4 is a graph showing the generation of the light intensity data of one of the plurality of first-sequence pixels and the number of sequences of the second sequence.
Fig. 5 is a graph showing the generation of the light intensity data of two first-sequence pixels and the sequence number of the second-sequence pixels among the plurality of first-sequence pixels.
FIG. 6 is a schematic block diagram illustrating one embodiment of a system for determining an image to be reconstructed for broad spectrum optical speckle auto-correlation imaging as provided herein.
Fig. 7 is a schematic block diagram illustrating one embodiment of an image reconstruction system provided herein.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "first," "second," and the like in the description and in the claims, are not used for any order, quantity, or importance, but are used for distinguishing between different elements. Likewise, the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one. "plurality" or "plurality" means two or more. Unless otherwise indicated, the terms "front," "rear," "lower," and/or "upper" and the like are merely for convenience of description and are not limited to one location or one spatial orientation. The word "comprising" or "comprises", and the like, means that elements or items appearing before "comprising" or "comprising" are encompassed by the element or item recited after "comprising" or "comprising" and equivalents thereof, and that other elements or items are not excluded. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
The method for determining the image to be reconstructed for wide-spectrum optical speckle autocorrelation imaging comprises the following steps: acquiring a sampling image of wide-spectrum optical speckle autocorrelation imaging; determining a primary screening size according to the sampling image, wherein the primary screening size is smaller than the size of the sampling image; determining a gradient sum of a plurality of prescreened pixel arrays having a prescreening size in the sampled image; acquiring a gradient and a central pixel of the central position of the maximum primary screening pixel array; acquiring a plurality of sub-sampling images with a plurality of different sub-sampling sizes by taking a central position corresponding to a central pixel as an image central position; if the peak background ratio of the minimum sub-sampling size in the plurality of different sub-sampling sizes is maximum, the primary screening size is redetermined; if the peak background ratio of the minimum sub-sampling size is not the maximum, determining the sub-sampling image with the maximum peak background ratio as an image to be reconstructed, and using the image to be reconstructed. The method comprises the steps of determining a primary screening size according to a sampling image, determining a plurality of primary screening pixel arrays in the sampling image, acquiring a gradient and a central pixel of the central position of the largest primary screening pixel array, screening the central position of an image to be reconstructed, which is close to an optical axis, faster, acquiring a plurality of sub-sampling images with different sub-sampling sizes by taking the central position corresponding to the central pixel as the central position of the image, and determining the sub-sampling image with the largest peak background ratio as the image to be reconstructed if the peak background ratio of the minimum sub-sampling size is not the largest, so that the image to be reconstructed with proper size can be obtained. Therefore, the image to be reconstructed, which is close to the optical axis and has proper size, can be obtained quickly, and the self-correlation imaging capability of the wide-spectrum optical speckle is improved.
The application provides a method for determining an image to be reconstructed of wide-spectrum optical speckle autocorrelation imaging. The method for determining the image to be reconstructed of the broad spectrum optical speckle autocorrelation imaging is described in detail below with reference to the accompanying drawings. The features of the examples and embodiments described below may be combined with each other without conflict.
FIG. 1 is a flow chart illustrating one embodiment of a method of determining an image to be reconstructed for broad spectrum optical speckle auto-correlation imaging of the present application. As shown in FIG. 1, the method for determining the image to be reconstructed by the broad spectrum optical speckle autocorrelation imaging comprises the steps 11-17. The broad spectrum optical speckle autocorrelation imaging is an image obtained by a speckle autocorrelation imaging method under broad spectrum illumination. Fig. 2 is a schematic diagram of broad spectrum optical speckle auto-correlation imaging. Specifically, fig. 2 shows the light intensity distribution of the object to be imaged on the photosensitive surface of the detector under the illumination of an LED with a bandwidth of 35nm and a center wavelength of 530 nm. The image to be reconstructed of the wide-spectrum optical speckle autocorrelation imaging is used for reconstructing the wide-spectrum optical speckle autocorrelation imaging through a phase recovery algorithm.
And step 11, acquiring a sampling image of wide-spectrum optical speckle autocorrelation imaging. And under wide-spectrum illumination, acquiring a speckle image formed by a target object to be imaged on a photosensitive surface of the detector, and taking the speckle image as a sampling image.
And step 12, determining a primary screening size according to the sampling image, wherein the primary screening size is smaller than the size of the sampling image. The primary screening size is the size of the primary screening when the image to be reconstructed is determined in the sampled image.
And step 17, if the peak background ratio of the minimum sub-sampling size is not the maximum, determining the sub-sampling image with the maximum peak background ratio as an image to be reconstructed, and using the image to reconstruct. If the sub-sampling image with the largest peak background ratio is not the minimum sub-sampling size, the sub-sampling image can provide more sufficient samples for reconstructing the image, and the sub-sampling image is determined as the image to be reconstructed. The position of the image to be reconstructed is closer to the optical axis, and the image to be reconstructed can be used for extracting effective imaging information of a target object and reconstructing the image. After the image is reconstructed, the position of the optical axis can be deduced.
In some embodiments, the preliminary screening size is determined according to the sampled image, a plurality of preliminary screening pixel arrays are determined in the sampled image, and central pixels of gradients and central positions of the largest preliminary screening pixel arrays are acquired, so that the central positions of the images to be reconstructed close to the optical axis can be screened out faster, sub-sampled images with different sub-sampling sizes taking the central positions corresponding to the central pixels as the central positions of the images are acquired, and if the peak background ratio of the smallest sub-sampling size is not the largest, the sub-sampled image with the largest peak background ratio is determined as the images to be reconstructed, and the images to be reconstructed with proper size can be obtained. Therefore, the image to be reconstructed, which is close to the optical axis and has proper size, can be obtained quickly, and the self-correlation imaging capability of the wide-spectrum optical speckle is improved.
FIG. 3 is a flow chart illustrating one embodiment of step 12 shown in FIG. 1. The sequence of sampling the pixel array of the image includes a first sequence and a second sequence, one of the first sequence and the second sequence being a row and the other being a column. Step 12 comprises: step 121 to step 124.
Step 124 includes: half the width of the sequence number of the second sequence with the largest overlap in full width at half maximum was determined as the primary screen size. In the embodiment shown in fig. 5, the width of line segment B overlapping line segment C, i.e., half the width of line segment D, is determined as the primary screen size. Thus, a suitable sizing for determining the array of sizing pixels may be obtained.
In the embodiment shown in fig. 3, step 16 includes: acquiring a plurality of pixels of a first sequence from a pixel array of the sampled image at intervals of a second set sequence number, wherein the second set sequence number is smaller than the first set sequence number; generating a graph of light intensity data for each of the plurality of first sequence pixels with a number of sequences of the second sequence; acquiring the full width at half maximum of the graph of each pixel of the first sequence; and determining the size of the primary screen according to the sequence number of the second sequence with the maximum half-width coincidence of the curve graphs of the pixels of the first sequence. When the peak background ratio of the minimum sub-sampling size in the plurality of different sub-sampling sizes is maximum and the primary screening size needs to be redetermined, acquiring a plurality of pixels of a first sequence every second set sequence number in a pixel array of the sampled image; the second set sequence number is smaller than the first set sequence number. For example, the pixel array size of the sampled image is 5120×5120, the first set of sequences is 640, and the second set of sequences is 540. The number of pixels of the first plurality of sequences selected every second set of sequences is greater than the number of pixels of the first plurality of sequences selected every first set of sequences. In this way, upon re-sizing the primary screen, more multiple first-order pixels arranged in the direction of the first-order can be obtained. The determined prescreening size is more suitable for screening the appropriate prescreening pixel array based on the number of sequences of the second sequence having the largest full width at half maximum overlap of the plot of pixels of the plurality of first sequences, thereby determining the appropriate subsampled size.
In some embodiments, the first sequence is a row and the second sequence is a column. Step 12 comprises: acquiring a plurality of columns of pixels from a pixel array of a sampling image at intervals of a first set column number; generating a graph of light intensity data and pixel row numbers for each of the columns of pixels; acquiring the full width at half maximum of the graph of each column of pixels; and determining the primary screening size according to the pixel row number with the maximum half-width coincidence of the curve graph of the multiple columns of pixels. The first set number of columns may be determined experimentally or empirically. For example, the pixel array size of the sampled image is 5120×5120, and the first set column number is 640. The pixel of the 1 st, 640 th, 1280 th, 1920 th, 2560 th, 3200 th, 3840 th, 4480 th and 5120 th columns are selected respectively. And generating a graph of the light intensity data of each column of pixels and the line number of the pixels. And acquiring the full width half maximum of each generated graph, and determining the primary screening size according to the pixel row number with the maximum overlapping of the full width half maximum of the graph.
Wherein, determining the primary screening size according to the pixel row number with the maximum half-width coincidence of the curve graph of the plurality of columns of pixels comprises: half the width of the pixel row number with the largest overlap in full width half maximum was determined as the primary screening size.
In this embodiment, step 16 includes: obtaining a plurality of columns of pixels from the pixel array of the sampling image at intervals of a second set column number, wherein the second set column number is smaller than the first set column number; generating a graph of light intensity data and pixel row numbers for each of the columns of pixels; acquiring the full width at half maximum of the graph of each column of pixels; and determining the primary screening size according to the pixel row number with the maximum half-width coincidence of the curve graph of the multiple columns of pixels. The second set column number is smaller than the first set column number, more columns of pixels can be obtained at intervals of the second set column number, and the size of the primary screen is redetermined according to the curve graph of the more columns of pixels.
In some embodiments, step 12 comprises: acquiring a plurality of rows of pixels from a pixel array of a sampling image at intervals of a first set row number; generating a graph of light intensity data and pixel column numbers of each row of pixels; acquiring the full width at half maximum of a graph of each row of pixels; and determining the primary screening size according to the pixel column number with the maximum half-width coincidence of the curve graph of the plurality of rows of pixels. The first set number of rows may be determined experimentally or empirically. For example, the pixel array size of the sampled image is 5120×5120, and the first set line number is 640. Rows of pixels of row 1, row 640, row 1280, row 1920, row 2560, row 3200, row 3840, row 4480, and row 5120 are selected, respectively. And generating a graph of the light intensity data of each row of pixels and the pixel column number. And acquiring the full width at half maximum of each generated graph, and determining the primary screening size according to the pixel column number with the maximum overlapping of the full width at half maximum of the graph.
Wherein, according to the most coincident pixel column number of full width half maximum of the graph of the multirow pixel, confirm the size of the preliminary screening, include: half the width of the pixel column with the largest overlap in full width half maximum was determined as the primary screening size.
In this embodiment, step 16 includes: acquiring a plurality of rows of pixels from a pixel array of the sampling image at intervals of a second set row number, wherein the second set row number is smaller than the first set row number; generating a graph of light intensity data and pixel column numbers of each row of pixels; acquiring the full width at half maximum of a graph of each row of pixels; and determining the primary screening size according to the pixel column number with the maximum half-width coincidence of the curve graph of the plurality of rows of pixels. The second set line number is smaller than the first set line number, the second set line number is spaced, more lines of pixels can be obtained, and the primary screening size is redetermined according to the graphs of the more lines of pixels.
As shown in fig. 6, the present application provides a system for determining an image to be reconstructed for broad spectrum optical speckle auto-correlation imaging, which includes one or more processors 21 for implementing the method for determining an image to be reconstructed for broad spectrum optical speckle auto-correlation imaging as described above. The above embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software.
In some embodiments, the system for determining an image to be reconstructed for broad spectrum optical speckle auto-correlation imaging may comprise a computer readable storage medium 22, the computer readable storage medium 22 may store a program that may be invoked by the processor 21, and may comprise a non-volatile storage medium. In some embodiments, the system for determining an image to be reconstructed for broad spectrum optical speckle auto-correlation imaging may include a memory 23 and an interface 24. In some embodiments, the system for determining the image to be reconstructed for broad spectrum optical speckle auto-correlation imaging may also include other hardware depending on the application.
The present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method of determining an image to be reconstructed for broad spectrum optical speckle auto-correlation imaging as described above. Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The application also provides an image reconstruction method, which comprises the following steps: a method of determining an image to be reconstructed of broad spectrum optical speckle autocorrelation imaging as hereinbefore described; and reconstructing an image according to the image to be reconstructed. After the image to be reconstructed is determined by the method for determining the image to be reconstructed of the broad spectrum optical speckle autocorrelation imaging, the image is reconstructed according to the image to be reconstructed by a phase recovery algorithm and other methods, so as to obtain the broad spectrum optical speckle autocorrelation imaging.
Fig. 7 shows an image reconstruction system provided herein, comprising one or more processors 31 for implementing the image reconstruction method as described above. The above embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software.
In some embodiments, the image reconstruction system may include a computer readable storage medium 32, and the computer readable storage medium 32 may store a program that may be called by the processor 31, and may include a nonvolatile storage medium. In some embodiments, the image reconstruction system may include a memory 33 and an interface 34. In some embodiments, the image reconstruction system may also include other hardware depending on the application.
The present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the image reconstruction method as described above. Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for determining an image to be reconstructed for broad spectrum optical speckle autocorrelation imaging, comprising:
acquiring a sampling image of the wide spectrum optical speckle autocorrelation imaging;
determining a primary screening size according to the sampling image, wherein the primary screening size is smaller than the size of the sampling image;
determining a sum of gradients of a plurality of prescreened pixel arrays having the prescreening size in the sampled image;
Acquiring a gradient and a maximum central pixel of the central position of the primary screening pixel array;
acquiring a plurality of sub-sampling images with a plurality of different sub-sampling sizes by taking the central position corresponding to the central pixel as an image central position;
if the peak background ratio of the minimum sub-sampling size in the plurality of different sub-sampling sizes is maximum, the primary screening size is redetermined;
and if the peak background ratio of the minimum sub-sampling size is not the maximum, determining the sub-sampling image with the maximum peak background ratio as the image to be reconstructed, and using the image to be reconstructed.
2. The method of claim 1, wherein the sequence of pixel arrays of the sampled images includes a first sequence and a second sequence, one of the first sequence and the second sequence being a row and the other being a column; the determining the primary screening size according to the sampling image comprises the following steps:
obtaining a plurality of pixels of a first sequence from the pixel array of the sampling image at intervals of a first set sequence number in the direction of the second sequence;
generating a graph of light intensity data for each of the plurality of first sequence pixels with the number of sequences of the second sequence;
Acquiring the full width at half maximum of the graph of each first sequence of pixels;
determining the primary screen size according to the number of sequences of the second sequence in which the full width at half maximum of the plots of pixels of the plurality of first sequences most coincides;
wherein said determining said primary screen size from said number of sequences of said second sequence for which said full width at half maximum of said plot of pixels of said plurality of first sequences most coincides comprises:
and determining half of the width of the sequence number of the second sequence with the largest half width overlapping as the primary screening size.
3. The method of claim 2, wherein the sub-sampling size comprises one-half of the primary screen size, two times the primary screen size, and a size between one-half of the primary screen size and two times the primary screen size.
4. The method of claim 2, wherein the re-determining the preliminary screening size if the peak-to-background ratio of the smallest sub-sample size of the plurality of different sub-sample sizes is largest comprises:
Obtaining a plurality of pixels of a first sequence from the pixel array of the sampled image at intervals of a second set sequence number in the direction of the second sequence, wherein the second set sequence number is smaller than the first set sequence number;
generating a graph of light intensity data for each first sequence of pixels of the plurality of first sequence pixels with a sequence number of the second sequence;
acquiring the full width at half maximum of the graph of each first sequence of pixels;
and determining the size of the primary screen according to the sequence number of the second sequence with the maximum half-width coincidence of the curve graph of the pixels of the plurality of first sequences.
5. The method of claim 2, wherein the first sequence is rows and the second sequence is columns, wherein the determining the preliminary screening size from the sampled image comprises:
acquiring a plurality of columns of pixels from the pixel array of the sampling image at intervals of a first set column number;
generating a graph of light intensity data and pixel row numbers for each column of pixels of the plurality of columns of pixels;
acquiring the full width at half maximum of the graph of each column of pixels;
Determining the primary screening size according to the pixel row number with the maximum half-width coincidence of the curve graph of the multi-column pixels;
wherein said determining said prescreen size based on said number of rows of pixels with said full width at half maximum coincidence of said plots of said plurality of columns of pixels comprises:
and determining half of the width of the pixel row number with the maximum half width overlapping as the primary screening size.
6. The method of claim 5, wherein the re-determining the preliminary screening size if the peak-to-background ratio of the smallest sub-sample size of the plurality of different sub-sample sizes is largest comprises:
obtaining a plurality of columns of pixels from the pixel array of the sampling image at intervals of a second set column number, wherein the second set column number is smaller than the first set column number;
generating a graph of light intensity data and pixel row numbers for each column of pixels of the plurality of columns of pixels;
acquiring the full width at half maximum of the graph of each column of pixels;
and determining the primary screening size according to the pixel row number with the maximum half-width coincidence of the curve graph of the pixel columns.
7. The method of claim 2, wherein the first sequence is rows and the second sequence is columns, wherein the determining the preliminary screening size from the sampled image comprises:
Acquiring a plurality of rows of pixels from the pixel array of the sampling image at intervals of a first set row number;
generating a graph of light intensity data and pixel column numbers of each row of pixels of the plurality of rows of pixels;
acquiring the full width at half maximum of the graph of each row of pixels;
determining the primary screening size according to the pixel column number with the maximum half-width coincidence of the curve graph of the plurality of rows of pixels;
wherein said determining said prescreen size based on said number of columns of pixels with the largest overlap of said full width at half maximum of said plot of said plurality of rows of pixels comprises:
and determining half of the width of the pixel column number with the maximum half width overlapping as the primary screening size.
8. The method of claim 7, wherein the re-determining the preliminary screening size if the peak-to-background ratio of the smallest sub-sample size of the plurality of different sub-sample sizes is largest comprises:
acquiring a plurality of rows of pixels from the pixel array of the sampling image at intervals of a second set row number, wherein the second set row number is smaller than the first set row number;
generating a graph of light intensity data and pixel column numbers of each row of pixels of the plurality of rows of pixels;
Acquiring the full width at half maximum of the graph of each row of pixels;
and determining the primary screening size according to the pixel column number with the maximum half-width coincidence of the curve graph of the plurality of rows of pixels.
9. The method of claim 1, wherein determining a sum of gradients of a plurality of prescreened pixel arrays having the prescreening size in the sampled image comprises:
traversing the pixel arrays of the sampled images according to the primary screening size to obtain a plurality of primary screening pixel arrays;
determining a sum of gradients of the plurality of arrays of primary screen pixels, respectively; or (b)
The determining a gradient sum of a plurality of arrays of prescreened pixels in the sampled image having the prescreened size comprises:
selecting the plurality of prescreened pixel arrays according to the prescreening size in the pixel arrays of the sampled image with at least one row and/or at least one column spacing;
the gradient sums of the plurality of arrays of primary screen pixels are determined separately.
10. The method for determining an image to be reconstructed for wide spectrum optical speckle auto-correlation imaging according to claim 1, wherein if the peak background ratio of the minimum sub-sampling size is not maximum, determining the sub-sampled image with the maximum peak background ratio as the image to be reconstructed comprises:
Respectively carrying out autocorrelation operation on the plurality of sub-sampling images; and
And respectively calculating peak background ratios of the sub-sampling images.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060104505A1 (en) * | 2004-11-15 | 2006-05-18 | Chih-Lung Chen | Demosaicking method and apparatus for color filter array interpolation in digital image acquisition systems |
| US20090118622A1 (en) * | 2007-11-06 | 2009-05-07 | The Regents Of The University Of California | APPARATUS AND METHOD FOR WIDEFIELD FUNCTIONAL IMAGING (WiFI) USING INTEGRATED STRUCTURED ILLUMINATION AND LASER SPECKLE IMAGING |
| US20190209116A1 (en) * | 2018-01-08 | 2019-07-11 | Progenics Pharmaceuticals, Inc. | Systems and methods for rapid neural network-based image segmentation and radiopharmaceutical uptake determination |
| CN111739139A (en) * | 2020-06-24 | 2020-10-02 | 西北大学 | Three-dimensional reconstruction method for noise CT image |
| CN111815757A (en) * | 2019-06-29 | 2020-10-23 | 浙江大学山东工业技术研究院 | Three-dimensional reconstruction method for large component based on image sequence |
| US20200378751A1 (en) * | 2019-05-29 | 2020-12-03 | Bae Systems Information And Electronic Systems Integration Inc. | Shearography detection and classification |
| CN112950731A (en) * | 2021-02-02 | 2021-06-11 | 西安电子科技大学 | Transmission scattering medium imaging method under single-frame strong background interference |
| CN113781592A (en) * | 2021-07-01 | 2021-12-10 | 杭州电子科技大学 | Speckle imaging reconstruction method based on complexity-guided phase recovery |
| CN114022365A (en) * | 2021-11-25 | 2022-02-08 | 中国科学院光电技术研究所 | Gradient descent speckle illumination super-resolution target reconstruction method |
| CN114862799A (en) * | 2022-05-10 | 2022-08-05 | 同心医联科技（北京）有限公司 | Full-automatic brain volume segmentation algorithm for FLAIR-MRI sequence |
| EP4056102A1 (en) * | 2021-03-09 | 2022-09-14 | Tata Consultancy Services Limited | System and method for imaging of localized and heterogeneous dynamics using laser speckle |
-
2023
- 2023-06-09 CN CN202310683539.0A patent/CN116402917B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060104505A1 (en) * | 2004-11-15 | 2006-05-18 | Chih-Lung Chen | Demosaicking method and apparatus for color filter array interpolation in digital image acquisition systems |
| US20090118622A1 (en) * | 2007-11-06 | 2009-05-07 | The Regents Of The University Of California | APPARATUS AND METHOD FOR WIDEFIELD FUNCTIONAL IMAGING (WiFI) USING INTEGRATED STRUCTURED ILLUMINATION AND LASER SPECKLE IMAGING |
| US20190209116A1 (en) * | 2018-01-08 | 2019-07-11 | Progenics Pharmaceuticals, Inc. | Systems and methods for rapid neural network-based image segmentation and radiopharmaceutical uptake determination |
| US20200378751A1 (en) * | 2019-05-29 | 2020-12-03 | Bae Systems Information And Electronic Systems Integration Inc. | Shearography detection and classification |
| CN111815757A (en) * | 2019-06-29 | 2020-10-23 | 浙江大学山东工业技术研究院 | Three-dimensional reconstruction method for large component based on image sequence |
| CN111739139A (en) * | 2020-06-24 | 2020-10-02 | 西北大学 | Three-dimensional reconstruction method for noise CT image |
| CN112950731A (en) * | 2021-02-02 | 2021-06-11 | 西安电子科技大学 | Transmission scattering medium imaging method under single-frame strong background interference |
| EP4056102A1 (en) * | 2021-03-09 | 2022-09-14 | Tata Consultancy Services Limited | System and method for imaging of localized and heterogeneous dynamics using laser speckle |
| CN113781592A (en) * | 2021-07-01 | 2021-12-10 | 杭州电子科技大学 | Speckle imaging reconstruction method based on complexity-guided phase recovery |
| CN114022365A (en) * | 2021-11-25 | 2022-02-08 | 中国科学院光电技术研究所 | Gradient descent speckle illumination super-resolution target reconstruction method |
| CN114862799A (en) * | 2022-05-10 | 2022-08-05 | 同心医联科技（北京）有限公司 | Full-automatic brain volume segmentation algorithm for FLAIR-MRI sequence |
Non-Patent Citations (4)
| Title |
|---|
| 崔丽君;赵成强;徐文东;焦佳;: "强度关联激光成像雷达测量矩阵研究", 中国激光, no. 09 * |
| 曾超;陈雨;: "车牌超分辨率重建与识别", 计算机测量与控制, no. 03 * |
| 汪道寅;胡访宇;: "基于改进SIFT算法的视频序列图像配准", 无线电工程, no. 02 * |
| 闫国庆;杨风暴;王肖霞;陶勇;李向燕;: "基于频域分解的融合鬼成像方法", 光子学报, no. 06 * |
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