CN115375597A - Microscopic image definition evaluation method combining time domain and frequency domain of NSST and variance - Google Patents
Microscopic image definition evaluation method combining time domain and frequency domain of NSST and variance Download PDFInfo
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Abstract
A microscopic image definition evaluation method combining NSST and variance time domain and frequency domain. The invention discloses an automatic focusing method of a microscope, which comprises the following steps: respectively acquiring corresponding initial images at different focusing points; loading the initial images; performing NSST (Non-subsampledheartearlet wave transform) decomposition on the images respectively, and generating subband coefficients (images); carrying out bilateral filtering and variance summation on the decomposed sub-band coefficients to obtain the energy sum of each sub-band coefficient, and finally obtaining a definition evaluation value according to the ratio of the high-frequency sub-band coefficient energy to the low-frequency sub-band coefficient energy; and determining the optimal focusing position of the current image by using the sharpness evaluation value. The invention provides an automatic focusing method of a microscope based on NSST decomposition, which can be effectively applied to automatic focusing of different microscope images. The invention can ensure that the microscope carries out automatic focusing and displays the clearest image, thereby greatly improving the reliability and the sensitivity of the focusing system.
Description
Technical Field
The present invention relates to an automatic focusing method for an image, and more particularly, to an automatic focusing method for a microscope image.
Background
Auto-focusing of microscope images is an important area of computer-aided diagnosis. If the rapid automatic focusing of the microscopic cell image is not available, the automatic acquisition efficiency of the microscopic cell image cannot be improved. With the development of scientific technology, the automatic focusing and computer-aided image processing of microscope images have become a trend. Computer-assisted medical diagnosis can not only ensure the objectivity and accuracy of medical diagnosis, but also save time and effort of medical experts, so that the research on the automatic focusing of microscope images and the computer-assisted medical diagnosis has very important theoretical and practical significance. Therefore, the clear microscope image obtained by the automatic focusing of the microscope image has important significance for future medical and other industries. Many researchers have also made progress in autofocusing and acquiring microscope images.
Over the past decades, many experts and researchers have proposed many autofocus algorithms, as shown in fig. 1. They can be divided into two groups: space domain and frequency domain auto-focusing algorithms. Laplace energy sum, variance and Sobel are the most common airspace automatic focusing algorithm, the image definition condition is judged by comparing the pixel value difference or jump of the image, and the method has the characteristics of short response time, large noise influence and the like; wavelet transform and discrete cosine transform are two common transforms of frequency domain auto-focusing algorithms, and sub-images with different frequency bands are obtained by decomposing images, so that different information can be reflected by the sub-images with different frequency bands, for example, noise is partially reflected in one frequency band, and image information is reflected in other frequency bands, and thus, the anti-noise performance of the algorithms can be improved by reducing the weights of different frequency bands.
An ideal autofocus algorithm should meet the following requirements: monotonous and sensitive to defocusing, single-peak focusing position, noise robustness, low computational complexity and independence from image content. Monotonicity, unimodal and robustness are considered as basic requirements; real-time applications require lower computational complexity; sensitivity to defocus and independence of image content are high-level requirements for auto-focus algorithms. In addition, since the captured image is inevitably contaminated by noise due to the environment and the image capturing device, the ideal auto-focusing algorithm should have a certain noise immunity. However, no autofocus algorithm meets all these requirements, especially not independently of the image content. Traditionally, auto-focus algorithms analyze the intensity variation of an image under the assumption that the focused image has the highest intensity variation. There is inevitably a correlation between the autofocus algorithm and the image content. If the images are not aligned, the autofocus algorithm will be affected by differences in image content and misidentification of the focused pixels may occur. Therefore, in order to overcome the above problems, it is of great significance to provide a more complete, fast and good anti-noise autofocus algorithm.
Disclosure of Invention
The invention aims to provide an automatic focusing algorithm of a microscope image with high speed and good anti-noise capability, which can finish automatic focusing on microscope images obtained under different conditions and obtain the clearest image by calculating a definition evaluation curve.
An NSST-based microscope image auto-focusing algorithm, the algorithm focusing process can include the following steps:
1. acquiring a plurality of microscope images under the condition of continuous X different object distances, wherein each image has the corresponding X object distance;
2. loading the obtained microscope image;
3. subjecting the obtained microscope image to N-stage NSST decomposition to obtain each image
A number of high frequency subband coefficients and a number of 1 low frequency subband coefficients;
4. calculating different frequency band coefficients and obtaining improved energy sum of different frequency bands 1 by calculating variance and bilateral filtering, wherein the bilateral filtering can well inhibit interference caused by noise, and the ratio of the energy sum of the high-frequency sub-band coefficient to the energy sum of the low-frequency sub-band coefficient is a definition evaluation function value;
5. obtaining the focal positions of microscope images with different object distances through the definition evaluation ratio;
the specific method of the step 3 comprises the following steps: the image is subjected to T-level non-downsampling pyramid (NSP) multi-scale decomposition through a non-downsampling pyramid filter to obtain T +1 sub-band images, wherein the T +1 sub-band images comprise 1 low-frequency image and k high-frequency images with different scales. And performing multi-directional decomposition on the sub-band image subjected to multi-scale decomposition by a non-downsampling direction filter bank, and ensuring that the image is not distorted by adopting a Shearlet Filter (SF) in the NSST direction decomposition, so that the image has translation invariance, and the pseudo-Gibbs effect is effectively inhibited. The subband coefficients of different scales and different directions obtained by multi-directional decomposition can be expressed as:
wherein N represents the number of NSST decomposition layers,
for the low frequency subband coefficients (L),
for each high frequency direction subband coefficient (H) in the n-scale T direction,
representing the number of directional decomposition levels at n scales,
indicating the number of directional subbands
The specific implementation method of the steps 4-6 is as follows: the variance of different frequency band coefficients is calculated
The formula for obtaining the energy sum of different sub-band coefficients is defined as follows:
wherein
Different sub-band coefficients obtained after the microscope image is subjected to NSST decomposition,
is the sub-band coefficient pixel average;
variance energy Sum (SV) of low and high frequency subband coefficients is obtained after NSST transform domain decomposition:
and accumulating and summing the high-frequency sub-band coefficients in different directions to obtain the energy sum of the high-frequency sub-band coefficients of the level:
and then adding the energy sums of the high-frequency sub-bands obtained by the accumulation and summation of all the levels to obtain the total energy sum of the high-frequency sub-bands of the NSST transform domain:
where s =0.8, n =3.
And finally, taking the ratio of the high-frequency sub-band energy to the low-frequency sub-band energy as a definition evaluation value:
whether the microscope image of the current object distance is the best focusing position or not is judged by comparing the definition evaluation values obtained by the microscope images of different object distances, namely whether the automatic focusing is finished or not is judged.
The invention has the following characteristics:
1. the invention provides an automatic focusing method of a microscope image based on NSST decomposition, which can be well applied to the automatic focusing of a microscope.
2. The core algorithm of the invention is simple to realize, can be completed by only one PC machine, and does not need excessive equipment requirements.
Drawings
FIG. 1 is a schematic illustration of evaluation focusing of two microscope images by a prior art autofocus technique;
FIG. 2 is a diagram illustrating the relationship between the evaluation result and the object distance of the conventional time-domain auto-focusing algorithm;
FIG. 3 is a diagram illustrating the relationship between the evaluation result of the conventional frequency-domain auto-focusing algorithm and the object distance;
FIG. 4 is a schematic diagram of the principles of the present invention;
FIG. 5 is a schematic flow diagram of the present invention;
FIG. 6 is a schematic diagram of the relationship between the evaluation result of the NSST-based auto-focusing algorithm and the object distance;
FIG. 7 is a graph comparing the results of the evaluation of the NSST-based autofocus algorithm of the present invention with other conventional algorithms;
FIG. 8 is a microscope image with no noise added and Gaussian noise added;
fig. 9 is a graph showing the normalized comparison of the evaluation results of the NSST-based autofocus algorithm according to the present invention with other conventional algorithms after gaussian noise is added.
Detailed Description
The basic process of the present invention is shown in fig. 4, and first, X initial images are obtained at X object distances of the microscope with different sizes, each image having its corresponding X-th object distance value, and X =3 can be seen in fig. 4. Loading the initial image, N-stage NSST decomposition of different X Zhang Chushi images, which makes each image available
A high frequency subband coefficient and 1Low-frequency self-band coefficients, N =3 in fig. 4. And calculating different frequency band coefficients and obtaining improved different frequency band energy sums by calculating the variance, wherein the ratio of the high-frequency sub-band coefficient energy sum to the low-frequency sub-band coefficient energy sum is a definition evaluation function value.
In fig. 4, taking X =3 as an example, initial images obtained by calculating initial images at different object distances are initial images
Initial image
Initial image
The ratio of the sum of the variance energy of the high-frequency signal and the low-frequency signal can be used to obtain a schematic diagram of the relationship between the evaluation result and the object distance of the NSST-based auto-focusing algorithm of the present invention as shown in fig. 5. Compared with the relationship obtained by some conventional auto-focusing algorithms, the curve of fig. 5 has a more definite focusing position and a better anti-noise performance. Finally, all the initial images are obtained
The relationship between the obtained definition evaluation value and the corresponding object distance is used for obtaining the size of the object distance when the definition evaluation value is maximum, namely the focal length.
In addition, the sum of variance of the sub-band images after NSST decomposition is solved, firstly, T-level non-downsampling pyramid (NSP) multi-scale decomposition is carried out on the images through a non-downsampling pyramid filter, and T +1 sub-band images are obtained, wherein the T +1 sub-band images comprise 1 low-frequency image and k high-frequency images with different scales. And performing multi-directional decomposition on the sub-band image subjected to multi-scale decomposition by a non-downsampling direction filter bank, and ensuring that the image is not distorted by adopting a Shearlet Filter (SF) in the NSST direction decomposition, so that the image has translation invariance, and the pseudo-Gibbs effect is effectively inhibited. The subband coefficients of different scales and different directions obtained by multi-directional decomposition can be expressed as:
wherein N represents the number of NSST decomposition layers,
for the low frequency subband coefficients (L),
for each high frequency direction subband coefficient (H) in the n-scale T direction,
representing the number of directional decomposition levels at n scales,
indicating the number of directional subbands
The specific implementation method of the steps 4-6 is as follows: the formula for obtaining the energy sum of different sub-band coefficients by solving the variance (SV) of different frequency band coefficients is defined as follows:
wherein
Different sub-band coefficients obtained after the microscope image is subjected to NSST decomposition,
is the sub-band coefficient pixel average;
variance energy Sum (SV) of low and high frequency subband coefficients is obtained after NSST transform domain decomposition:
and accumulating and summing the high-frequency sub-band coefficients in different directions to obtain the energy sum of the high-frequency sub-band coefficients of the level:
and then adding the energy sums of the high-frequency sub-bands obtained by the accumulation and summation of all the levels to obtain the total energy sum of the high-frequency sub-bands of the NSST transform domain:
where s =0.8, n =3.
And finally, taking the ratio of the high-frequency sub-band energy to the low-frequency sub-band energy as a definition evaluation value:
It can be seen from the above table that the narrow width can objectively reflect the steepness of the definition evaluation curve, and the larger the narrow width is, the steeper the curve is, i.e. the algorithm performance is better. In 11 sets of test data, 8 sets of narrow widths of the NSST algorithm reach the highest, 2 sets of narrow widths of the NSST algorithm reach the second highest, and the narrow widths can be higher than the lowest numerical value by about 100% and higher than the second highest numerical value by about 10% -20%, so that the NSST algorithm is better than other algorithms in the steepness degree of the definition evaluation curve, namely the curve is narrower. In both of the other two sets of data, the NSST algorithm achieved the highest or second highest narrow width.
In order to analyze the advantages, disadvantages and noise resistance of the experimental algorithm to the maximum extent, gaussian noise is added to each image, and in order to improve the noise resistance of the algorithm better, after the Gaussian noise is added, bilateral filtering processing is performed on the image once. The image added with gaussian noise and the original image are shown in fig. 8, and the results are subjected to curve normalization, and a normalized sharpness evaluation curve is shown in fig. 9. As can be seen from fig. 9, after gaussian noise is added to the image, the prior art algorithm can find the global maximum, but the sharpness curve still has fluctuation, and the NSST algorithm proposed herein does not have such problem. In addition to this, for a narrow width in which curve steepness can be judged
After noise is added, the existing algorithms all show up and down fluctuation with different degrees, so that the narrow width cannot be or is difficult to obtain, but the NSST algorithm has no problem, and the situation can well reflect that the anti-noise performance of the used NSST algorithm is better than that of other comparison algorithms.
Claims (6)
1. An NSST-based microscope image auto-focusing algorithm, the algorithm focusing process can include the following steps:
a) Acquiring a plurality of microscope images under the condition of continuous X different object distances, wherein each image has the corresponding X object distance;
b) Loading the obtained microscope image; c) Subjecting the obtained microscope image to N-stage NSST decomposition to obtain each image
A number of high frequency subband coefficients and a number of 1 low frequency subband coefficients; d) Calculating different frequency band coefficients and obtaining improved energy sums of different frequency bands by calculating variance and bilateral filtering, wherein the bilateral filtering can well inhibit interference caused by noise, and the ratio of the energy sum of the high-frequency sub-band coefficient to the energy sum of the low-frequency sub-band coefficient is a definition evaluation function value; e) In order to test the anti-noise performance of the algorithm, the steps b-d are repeated after Gaussian noise is added to the original image; f) And drawing a normalized definition evaluation curve through the definition evaluation ratio to obtain the focus positions of the microscope images with different object distances.
2. The method for performing NSST decomposition on an image according to step c of claim 1 comprises: carrying out T-level non-downsampling pyramid (NSP) multi-scale decomposition on an image through a non-downsampling tower filter to obtain T +1 sub-band images, wherein the T +1 sub-band images comprise 1 low-frequency image and k high-frequency images with different scales; performing multi-direction decomposition on the sub-band image subjected to multi-scale decomposition through a non-downsampling direction filter bank, and ensuring that the image is not distorted by adopting a Shearlet Filter (SF) in NSST direction decomposition, so that the image has translation invariance, and the pseudo-Gibbs effect is effectively inhibited; the subband coefficients of different scales and different directions obtained by multi-directional decomposition can be expressed as:
wherein N represents the number of NSST decomposition layers,
for the low frequency subband coefficients (L),
for each high frequency direction subband coefficient (H) in the n-scale T direction,
representing the number of directional decomposition levels at n scales,
indicating the number of directional subbands.
3. The method for calculating the sum of variance in step d according to claim 1 comprises: the formula for obtaining the energy sum of different sub-band coefficients by solving the variance (SV) of different frequency band coefficients is defined as follows:
wherein
Different sub-band coefficients obtained after the microscope image is subjected to NSST decomposition,
is the sub-band coefficient pixel average;
variance energy Sum (SV) of low and high frequency subband coefficients is obtained after NSST transform domain decomposition:
4. the method for testing the anti-noise performance in the step e of claim 1 comprises the following specific steps: in order to analyze the superiority, the inferiority and the noise immunity of the experimental algorithm to the maximum extent, d =0.1 Gaussian noise is added to each image, and in order to improve the noise immunity of the algorithm better, the image is subjected to bilateral filtering processing after the noise is added.
5. The method for determining the image sharpness evaluation value according to step f of claim 1 comprises: accumulating and summing the high-frequency sub-band coefficients in different directions to obtain the energy sum of the high-frequency sub-band coefficients of the level:
and then adding the energy sums of the high-frequency sub-bands obtained by the accumulation and summation of all the levels to obtain the total energy sum of the high-frequency sub-bands of the NSST transform domain:
wherein s =0.8, n =3; and finally, taking the ratio of the high-frequency sub-band energy to the low-frequency sub-band energy as a definition evaluation value:
6. The method of claim 5, wherein the ratio of the widths of the evaluation curves at 40% and 80% normalized to each other is defined as a narrow width
(ii) a And the superiority and inferiority of the algorithm are objectively reflected according to the numerical value, the narrow width can reflect the steepness of a definition evaluation curve, and the steeper the narrow width is, the steeper the curve is, namely, the algorithm performance is better.
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| CN116400490B (en) * | 2023-06-08 | 2023-08-25 | 杭州华得森生物技术有限公司 | Fluorescence microscopic imaging system and method thereof |
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