CN108305320B - Self-adaptive sliding window reconstruction method for improving large-field holographic imaging quality - Google Patents
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Abstract
The invention relates to a self-adaptive sliding window reconstruction method for improving the quality of large-field holographic imaging, and belongs to the technical field of digital holographic imaging. Firstly, preprocessing and cutting an original hologram; then, carrying out single non-overlapping blocking on the whole image by using a conventional blocking method, completing reconstruction of each block, and recording the gray average value of each block; then calculating the separation rate of each gray average value set and setting the window moving step length according to the separation rate; and finally, the sliding window is reconstructed in blocks according to the set step length, and the gray levels of the overlapped corresponding pixel points of each block are assigned as the superposition average value, so that background interference items with obvious changes are counteracted, and relatively fixed object information is reserved. The invention enhances the fidelity and integrity of the object information after reconstruction of the large-size hologram, better meets the observation and measurement of the large-view microscopic object, and lays a foundation for the analysis and extraction of the subsequent object information.
Description
Technical Field
The invention belongs to the technical field of reconstruction of digital holograms, and relates to a sliding window block reconstruction method for a large-size hologram for large-field imaging.
Background
Digital holography is an emerging imaging method with the precision reaching the micron level in recent years, and the method has the advantages of full view field, non-contact, no damage, real-time property and quantification, and is used for directly recording and obtaining a sample image by optical components such as a CCD (charge coupled device). Because the color of the sample can be clearly imaged without the need of color imaging, the method is particularly suitable for quantitative three-dimensional reconstruction and rapid tracking of living biological samples, and can be rapidly developed in the field of biomedical application, particularly cell culture observation.
The hologram imaging field of view directly corresponds to the size of the optical element CCD, so the hologram imaging field of view has the advantage of large-field imaging due to the inherent thickness of the hologram imaging field of view. On the premise of accurately imaging the micron-level object, the object information recorded under the hologram field is much richer than that of a conventional optical microscope, and the hologram has advantages in the aspects of observation and recording of a large sample micro object group.
However, one problem that cannot be avoided in reconstructing large-size holograms is that the image blocks must be partitioned and then the convolution reconstruction of each image block must be performed one by one. Experiments prove that too large blocks or direct reconstruction of the whole hologram always introduces too much peripheral irrelevant light field information during reconstruction, and then transverse and longitudinal strips and background patches which are difficult to remove are formed. In addition, considering that the convolution reconstruction method needs a large amount of forward and inverse Fourier transforms, and the fast Fourier transform has the highest operation efficiency on signals with the length of 2 raised to the power of an integer, the comprehensive consideration is that the reconstruction effect is the best when 256 pixels or 512 pixels are selected as a single block, and the poor effect is caused when the size is too large or too small.
Along with image segmentation, some objects are always located at the edge positions of segmentation, the diffraction rings are artificially split and distributed on different image sub-blocks, object information is damaged after the incomplete diffraction rings are reconstructed, the real appearance of the objects is difficult to reflect, erroneous judgment and missing judgment can be caused after observation of experimenters, and even experimental failure is caused. In addition, under the influence of object distribution, the overall brightness difference after reconstruction between different blocks is often very obvious, the block traces are obvious, the comparability of objects in different image sub-blocks is influenced, and huge interference and challenge are formed for observation and analysis work of experimenters. Therefore, a sliding window blocking reconstruction method for large-size holograms for large-field imaging is needed to improve the quality of the reconstructed image and the fidelity of the object information.
Disclosure of Invention
In view of this, the present invention provides a block processing strategy based on an adaptive sliding window, which ensures that an object located on a block boundary in a hologram observation region always falls into a next image block after a window moves through mutually overlapped blocks, thereby ensuring that object information can be completely retained in at least one reconstruction process, and the number of times that the object information can be completely retained after reconstruction increases along with the refinement of a sliding step length, so as to achieve the purpose of improving the quality of the reconstructed image and the fidelity of the object information.
In order to achieve the purpose, the invention provides the following technical scheme:
a self-adaptive sliding window reconstruction method for improving the quality of large-field holographic imaging comprises the steps of firstly preprocessing and cutting an original hologram; then, carrying out single non-overlapping blocking on the whole image by using a conventional blocking method, completing reconstruction of each block, and recording the gray average value of each block; then calculating the separation rate of each gray average value set and setting the window moving step length according to the separation rate; finally, the sliding window body is reconstructed in blocks according to the set step length, and the gray levels of the overlapped corresponding pixel points of each block are assigned as the superposition average value, so that background interference items with obvious changes are counteracted, and relatively fixed object information is reserved;
the method specifically comprises the following steps:
s1: reading in a hologram to be reconstructed, and preprocessing the hologram;
s2: setting the block size, and cutting the image;
s3: performing primary blocking and reconstruction, recording the average brightness of each block, and adaptively setting the window sliding step length according to the data separation rate;
s4: moving the window according to the block size set in S1 and the self-adaptive step length calculated in S3, performing window traversal on the effective region of the image, and reconstructing the image block in each window;
s5: and correspondingly dividing the data at the same coordinate position of the two matrixes obtained by processing in the S4 to obtain a superposed and averaged reconstructed image Rec which is finally subjected to the adaptive sliding window blocking processing.
Further, the step S1 specifically includes:
mapping the image to a gray image from an RGB color space, wherein a conversion formula from the image RGB space to the gray is shown as the following formula, wherein i and j are row-column coordinates, and R, G, B are information of red, green and blue channels in three primary colors respectively:
Gray(i,j)＝0.229×R(i,j)+0.587×G(i,j)+0.114×B(i,j)。
further, the step S2 specifically includes:
s21: setting the block side length of an image block as L, the row and column values of the whole image as M and N, and the unit is a pixel (pixel); and correspondingly dividing the image blocks Row and Col which can be arranged in the rows and columns by integer:
Row＝M|L
Col＝N|L
s22: and taking the middle Row multiplied by L Row and Col multiplied by L column area of the whole image as an effective reconstruction area to finish image cutting.
Further, the step S3 specifically includes:
s31: performing non-overlapping initial arrangement of image blocks in the effective area, namely reconstructing each image block one by using a traditional blocking method, and recording the average brightness value of each reconstructed image block in a Row x Col matrix;
s32: the mean intensity data recorded in the matrix were analyzed for outliers (Outlier) using the current international universal standard: taking an Inter-quartile Range (IQR) which is 1.5 times lower than that of a box body on a box diagram (Boxplot) or a quartile Range (IQR) which is 1.5 times higher than that of the box body on the box diagram as an outlier limit;
s33: counting the number Num of outliersoutAnd calculating the separation rate Ratio of the average gray value of each blockout：
S34: and setting an adaptive window sliding Step according to the data separation rate, wherein [. cndot. ] represents rounding:
the reason for setting the sliding step is that the higher the separation rate of the average gray scale value of each block of the image, the larger the difference between the blocks after the image block is reconstructed, and a more detailed sliding window is required for processing.
Further, the step S4 specifically includes:
when a certain holographic image block slides in the row direction p times and in the column direction q times, the leftmost pixel point of the whole hologram effective area is taken as a reference point and is marked as (1,1), and then the row of the image block: l; the method comprises the following steps: l, corresponding to the range covered in the hologram effective area, row: (p-1) × L +1 to pxl; the method comprises the following steps: (q-1) × L +1 to q × L;
two empty matrixes Matrix1 and Matrix2 with the same size as the effective area of the hologram are arranged and are respectively used for superposing the reconstruction graph rec of each holographic pattern blockp,qPixel gray values and superposition times at corresponding positions;
wherein the image is as follows: x is (p-1) × L + i, and Y is (q-1) × L + j.
Further, in step S5, the reconstructed image Rec is: rec (X, Y) ═ Marix1(X, Y)/Matrix2(X, Y).
Further, for hologram reconstruction in steps S1 and S4, a convolution reconstruction method is adopted, and the specific flow is as follows:
the convolution reconstruction method is constructed based on a linear system theory and a Rayleigh-Sommerfeld integral formula, can accurately reflect the spatial diffraction distribution of light, and is characterized in that the Rayleigh-Sommerfeld integral formula corresponds to light waves after diffraction information in a hologram is reproduced:
wherein the content of the first and second substances,
ima here denotes the unit of an imaginary number, λ is the wavelength of the reconstruction light, ziIs the distance between the object and the CCD, i.e. the reconstruction distance, wherein (x, y) is the horizontal and vertical coordinates of the recording surface, (x)i,yi) Position distribution on a reproduction image plane for the reconstructed image; h (x, y) is the light intensity distribution of the hologram recorded by the image sensor, namely the hologram obtained by collection and recording; c (x, y) is the distribution of the reconstructed light waves in the sensor plane, taken as the conjugate of R (x, y):
R(x,y)＝exp[-ima·k(xsinα+ysinβ)]
wherein k is 2 pi/lambda, and alpha and beta are respectively included angles between the plane reference light wave and the space yoz plane and the xoz plane;
according to the linear system theory, the complex amplitude of the reconstructed image is represented as a convolution as follows:
wherein g is a free space impulse response;
and then, by the convolution theory, a reproduced image is obtained by using Fourier transform calculation, and a reconstructed image is obtained:
U＝FT-1[FT(H·C)·FT(g)]。
the invention has the beneficial effects that: the invention enhances the fidelity of the reconstruction result of the large-size hologram, perfects the information of the covered object, and ensures that the visual perception of the whole hologram is more true and natural, thereby greatly facilitating the observation and recording of experimenters.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a flow chart of a sliding window reconstruction method according to the present invention;
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of an adaptive sliding window reconstruction method for improving the quality of large-field-of-view holographic imaging, and as shown in fig. 1, an adaptive sliding window reconstruction method for improving the quality of large-field-of-view holographic imaging specifically includes the following steps:
s1: reading in a hologram to be reconstructed, and preprocessing the hologram;
mapping the image to a gray image from an RGB color space, wherein a conversion formula from the image RGB space to the gray is shown as the following formula, wherein i and j are row-column coordinates, and R, G, B are information of red, green and blue channels in three primary colors respectively:
Gray(i,j)＝0.229×R(i,j)+0.587×G(i,j)+0.114×B(i,j)。
s2: setting the block size, and cutting the image;
s21: setting the block side length of an image block as L, the row and column values of the whole image as M and N, and the unit is a pixel (pixel); and correspondingly dividing the image blocks Row and Col which can be arranged in the rows and columns by integer:
Row＝M|L
Col＝N|L
s22: and taking the middle Row multiplied by L Row and Col multiplied by L column area of the whole image as an effective reconstruction area to finish image cutting.
S3: performing primary blocking and reconstruction, recording the average brightness of each block, and adaptively setting the window sliding step length according to the data separation rate;
s31: performing non-overlapping initial arrangement of image blocks in the effective area, namely reconstructing each image block one by using a traditional blocking method, and recording the average brightness value of each reconstructed image block in a Row x Col matrix;
s32: the mean intensity data recorded in the matrix were analyzed for outliers (Outlier) using the current international universal standard: taking an Inter-quartile Range (IQR) which is 1.5 times lower than that of a box body on a box diagram (Boxplot) or a quartile Range (IQR) which is 1.5 times higher than that of the box body on the box diagram as an outlier limit;
s33: counting the number Num of outliersoutAnd calculating the separation rate Ratio of the average gray value of each blockout：
S34: and setting an adaptive window sliding Step according to the data separation rate, wherein [. cndot. ] represents rounding:
the reason for setting the sliding step is that the higher the separation rate of the average gray scale value of each block of the image, the larger the difference between the blocks after the image block is reconstructed, and a more detailed sliding window is required for processing.
S4: moving the window according to the block size set in S1 and the self-adaptive step length calculated in S3, performing window traversal on the effective region of the image, and reconstructing the image block in each window;
when a certain holographic image block slides in the row direction p times and in the column direction q times, the leftmost pixel point of the whole hologram effective area is taken as a reference point and is marked as (1,1), and then the row of the image block: l; the method comprises the following steps: l, corresponding to the range covered in the hologram effective area, row: (p-1) × L +1 to pxl; the method comprises the following steps: (q-1) × L +1 to q × L;
two empty matrixes Matrix1 and Matrix2 with the same size as the effective area of the hologram are arranged and are respectively used for superposing the reconstruction graph rec of each holographic pattern blockp,qPixel gray values and superposition times at corresponding positions;
wherein the image is as follows: x is (p-1) × L + i, and Y is (q-1) × L + j.
S5: correspondingly dividing the data at the same coordinate position of the two matrixes obtained by processing in the step S4 to obtain a final reconstructed image Rec which is subjected to self-adaptive sliding window blocking processing and then is superposed and averaged as follows: rec (X, Y) ═ Marix1(X, Y)/Matrix2(X, Y).
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (7)
1. A self-adaptive sliding window reconstruction method for improving the quality of large-field holographic imaging is characterized in that the method comprises the steps of firstly preprocessing and cutting an original hologram; then, carrying out single non-overlapping blocking on the whole image by using a conventional blocking method, completing reconstruction of each block, and recording the gray average value of each block; then calculating the separation rate of each gray average value set and setting the window moving step length according to the separation rate; finally, the sliding window body is reconstructed in blocks according to the set step length, and the gray levels of the overlapped corresponding pixel points of each block are assigned as the superposition average value, so that background interference items with obvious changes are counteracted, and relatively fixed object information is reserved;
the method specifically comprises the following steps:
s1: reading in a hologram to be reconstructed, and preprocessing the hologram;
s2: setting the block size, and cutting the image;
s3: performing primary blocking and reconstruction, recording the average brightness of each block, and adaptively setting the window sliding step length according to the data separation rate;
s4: moving the window according to the block size set in S2 and the self-adaptive step length calculated in S3, performing window traversal on the effective region of the image, and reconstructing the image block in each window;
s5: and correspondingly dividing the data at the same coordinate position of the two matrixes obtained by processing in the S4 to obtain a superposed and averaged reconstructed image Rec which is finally subjected to the adaptive sliding window blocking processing.
2. The adaptive sliding window reconstruction method for improving the quality of large-field holographic imaging according to claim 1, wherein the step S1 specifically comprises:
mapping the image to a gray image from an RGB color space, wherein a conversion formula from the image RGB space to the gray is shown as the following formula, wherein i and j are row-column coordinates, and R, G, B are information of red, green and blue channels in three primary colors respectively:
Gray(i,j)＝0.229×R(i,j)+0.587×G(i,j)+0.114×B(i,j)。
3. the adaptive sliding window reconstruction method for improving the quality of large-field holographic imaging according to claim 1, wherein the step S2 specifically comprises:
s21: setting the block side length of an image block as L, the row and column values of the whole image as M and N, and the unit is a pixel (pixel); and correspondingly dividing the image blocks Row and Col which can be arranged in the rows and columns by integer:
Row＝M|L
Col＝N|L
s22: and taking the middle Row multiplied by L Row and Col multiplied by L column area of the whole image as an effective reconstruction area to finish image cutting.
4. The adaptive sliding window reconstruction method for improving the quality of large-field holographic imaging according to claim 3, wherein the step S3 specifically comprises:
s31: performing non-overlapping initial arrangement of image blocks in the effective area, namely reconstructing each image block one by using a traditional blocking method, and recording the average brightness value of each reconstructed image block in a Row x Col matrix;
s32: the mean intensity data recorded in the matrix were analyzed for outliers (Outlier) using the current international universal standard: taking an Inter-quartile Range (IQR) which is 1.5 times lower than that of a box body on a box diagram (Boxplot) or a quartile Range (IQR) which is 1.5 times higher than that of the box body on the box diagram as an outlier limit;
s33: counting the number Num of outliersoutAnd calculating the separation rate Ratio of the average gray value of each blockout：
S34: and setting an adaptive window sliding Step according to the data separation rate, wherein [. cndot. ] represents rounding:
5. the adaptive sliding window reconstruction method for improving the quality of large-field holographic imaging according to claim 1, wherein the step S4 specifically comprises:
when a certain holographic image block slides in the row direction p times and in the column direction q times, the leftmost pixel point of the whole hologram effective area is taken as a reference point and is marked as (1,1), and then the row of the image block: l; the method comprises the following steps: l, corresponding to the range covered in the hologram effective area, row: (p-1) × L +1 to pxl; the method comprises the following steps: (q-1) × L +1 to q × L;
two empty matrices Matrix1 and Matrix are provided which are as large as the effective area of the hologramix2 for superimposing the hologram reconstruction patterns recp,qPixel gray values and superposition times at corresponding positions;
wherein the image is as follows: x is (p-1) × L + i, and Y is (q-1) × L + j.
6. The adaptive sliding window reconstruction method for improving the quality of large-field holographic imaging according to claim 5, wherein in step S5, the reconstructed image Rec is:
Rec(X,Y)＝Marix1(X,Y)/Matrix2(X,Y)。
7. the adaptive sliding window reconstruction method for improving the quality of large-field holographic imaging according to claim 1, wherein a convolution reconstruction method is adopted for the reconstruction of the hologram in each of the steps S1 and S4, and the specific procedures are as follows:
the convolution reconstruction method is constructed based on a linear system theory and a Rayleigh-Sommerfeld integral formula, can accurately reflect the spatial diffraction distribution of light, and is characterized in that the Rayleigh-Sommerfeld integral formula corresponds to light waves after diffraction information in a hologram is reproduced:
wherein the content of the first and second substances,
ima here denotes the unit of an imaginary number, λ is the wavelength of the reconstruction light, ziIs the distance between the object and the CCD, i.e. the reconstruction distance, wherein (x, y) is the horizontal and vertical coordinates of the recording surface, (x)i,yi) Position distribution on a reproduction image plane for the reconstructed image; h (x, y) is the light intensity distribution of the hologram recorded by the image sensor, namely the hologram obtained by collection and recording; c (x, y) is the reconstructed lightThe distribution of the wave in the sensor plane, taken as the conjugate of R (x, y):
R(x,y)＝exp[-ima·k(xsinα+ysinβ)]
wherein k is 2 pi/lambda, and alpha and beta are respectively included angles between the plane reference light wave and the space yoz plane and the xoz plane;
according to the linear system theory, the complex amplitude of the reconstructed image is represented as a convolution as follows:
wherein g is a free space impulse response;
and then, by the convolution theory, a reproduced image is obtained by using Fourier transform calculation, and a reconstructed image is obtained:
U＝FT-1[FT(H·C)·FT(g)]。
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