Quantum imaging system and method for penetrating scattering media based on optical modulation and genetic algorithm

Abstract

The present invention relates to the field of quantum imaging technology, and more specifically, to a system and method for quantum imaging through scattering media based on light modulation and a genetic algorithm. The system and method include a generation and measurement unit, an optimization and shaping unit, and a verification and evaluation unit. The system modulates a spatial light modulator using a modulation signal to obtain initial modulated light, measures the initial modulated light using a detector, and obtains an initial image. The maximum and minimum values ​​of the intensity distribution of the initial image are obtained from a histogram of the initial image, and the contrast of the initial image is calculated based on the maximum and minimum values ​​of the intensity distribution of the initial image. An objective function is then defined based on the contrast value of the initial image. A control center introduces a genetic algorithm based on the objective function to optimize the initial modulation signal to find a modulation signal that maximizes the difference between bright and dark areas in the intensity distribution of the initial image, and calculates the peak signal-to-noise ratio of a final image generated corresponding to the modulation signal to evaluate the imaging quality.

Description

Technical Field

[0001] The present invention relates to the field of quantum imaging technology, and in particular to a system and method for quantum imaging through scattering media based on light modulation and genetic algorithm. Background Art

[0002] Quantum imaging is an important technology that can effectively reduce the impact of noise on imaging quality, especially in low-light conditions. By utilizing the quantum interference effect, this can improve the detectability of the signal and reduce interference with the imaged object. It is very suitable for use in medical and biological research and can acquire data without damaging the sample.

[0003] In many practical applications, the imaging process is often interfered with by scattering media, resulting in a decrease in image quality. For example, in medical imaging, the scattering of light by human tissue can blur the image, making it difficult to accurately diagnose diseases. Traditional imaging methods often work poorly when facing scattering media and cannot effectively penetrate the scattering medium to obtain a clear image. Quantum imaging technology, as an emerging imaging technology, has unique advantages. By modulating the incident light and combining it with a genetic algorithm to find a modulation signal that maximizes the difference between bright and dark areas in the initial image intensity distribution, the imaging quality can be effectively improved. To address this problem, we provide a quantum imaging system and method for penetrating scattering media based on light modulation and genetic algorithms. Summary of the Invention

[0004] The object of the present invention is to provide a system and method for quantum imaging through scattering media based on light modulation and genetic algorithm, so as to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, a quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm is provided, which includes a generation and measurement unit, an optimization and shaping unit, and a verification and evaluation unit;

[0006] The generating and measuring unit sends the incident light generated by the laser to the spatial light modulator, and the spatial light modulator modulates the incident light according to the initial modulation signal sent by the control center to form an initial modulated light, and uses a detector to measure the initial modulated light to obtain the size and pixel intensity value of the initial image;

[0007] The optimization shaping unit obtains the intensity distribution of the initial image based on the size and pixel intensity values ​​of the initial image, and feeds back the initial modulation signal corresponding to the initial image to the control center. The control center uses a genetic algorithm to iteratively update and optimize the initial modulation signal until a modulation signal is obtained that maximizes the difference between bright and dark areas in the intensity distribution of the initial image.

[0008] The verification and evaluation unit sends a modulation signal to a spatial light modulator through a control center. The spatial light modulator modulates the incident light according to the modulation signal to form modulated light. The modulated light passes through a scattering medium to form a final image. Finally, the imaging quality is evaluated by comparing the peak signal-to-noise ratio of the initial image and the final image.

[0009] As a further improvement of the present technical solution, the spatial light modulator in the generating and measuring unit modulates the incident light according to the modulation signal sent by the control center to form the modulated light, specifically including:

[0010] The spatial light modulator is a liquid crystal spatial light modulator, which uses the birefringence characteristics of liquid crystal molecules to control the phase of the incident light. When the arrangement of the liquid crystal molecules changes under the action of different electric fields, their refractive index will also change accordingly, causing the phase of the incident light to change when passing through the liquid crystal layer, forming modulated light.

[0011] As a further improvement of this technical solution, the steps of optimizing the operation of the shaping unit are as follows:

[0012] A1. Analyze the intensity distribution of the initial image based on the size and pixel value intensity of the initial image to obtain the maximum and minimum values ​​of the initial image intensity distribution, and calculate the contrast value of the initial image based on the maximum and minimum values ​​of the initial image intensity distribution. Then, define an objective function based on the contrast value of the initial image and feed it back to the control center. The objective function is the modulation signal corresponding to the maximum contrast that maximizes the difference between bright and dark areas in the intensity distribution of the initial image.

[0013] A2. The control center uses a genetic algorithm to optimize the initial modulation signal. Specifically, a group of initial modulation signals of spatial light modulators are randomly selected to form a genetic algorithm population. Each modulation unit of the spatial light modulator represents an individual in the genetic algorithm population. For each individual in the genetic algorithm population, the corresponding modulation unit is used to modulate the incident light to obtain optimized modulated light. The optimized modulated light is then passed through a scattering medium to form an optimized image. The intensity distribution of the optimized image is measured using a detector. Finally, the contrast value of the optimized image is calculated based on the intensity distribution of the optimized image, and the contrast value of the optimized image is used as the fitness value of the individual in the genetic algorithm population.

[0014] A3. Sort individuals according to their fitness values ​​and select individuals with high fitness values ​​as parents for crossover operation to generate new offspring individuals. Then perform mutation operation on the newly generated offspring individuals and add them to the population to replace individuals with low fitness values ​​to form a new population. The next iteration is performed until the maximum contrast value changes less than the contrast value threshold in several consecutive iterations. Then the iterative optimization is stopped.

[0015] As a further improvement of the present technical solution, the method of calculating the contrast value of the initial image according to the maximum and minimum values ​​of the intensity distribution of the initial image in S1 specifically includes:

[0016] Assume that the initial image has L different intensity levels, the intensity levels are arrive , the size of the initial image measured by the detector is , the intensity value of each pixel is ,in, , , the intensity value of each pixel is Substitute the formula to calculate the histogram of the initial image , the specific calculation formula is:

[0017] ,

[0018] in, , is the histogram of the initial image function, when hour, , otherwise 0, traverse the histogram of the initial image , find the first non-zero The value is the minimum value , find the last non-zero value, which is the maximum value , the maximum value of the initial image intensity distribution and minimum value Substitute the formula to calculate the contrast value of the initial image , the specific calculation formula is:

[0019] ,

[0020] The contrast value of the initial image The contrast value parameter used for the difference between the bright and dark areas in the initial image is maximized as the objective function. By adjusting the modulation signal of the spatial light modulator, the modulated initial image has a higher contrast value.

[0021] As a further improvement of the present technical solution, the control center in S2 introduces a genetic algorithm to optimize the modulation signal, specifically including:

[0022] The number of pixels of the spatial light modulator is recorded as , where each pixel can control the phase of the incident light. Now the modulation signal emitted by the control center is set to a length of A vector where each element represents the phase value of a pixel, and the phase value range is set to , randomly selected according to the phase value range The different lengths are As the initial population of the genetic algorithm, for the first individual in the initial population, let its modulation signal be vector ,in It is represented as the phase value of the i-th pixel. The spatial light modulator adjusts the phase of the incident light according to these phase values ​​to obtain the optimized modulated light. The contrast value of the image is calculated according to the intensity distribution of the optimized modulated light and used as the fitness value of the individual. The specific calculation formula is:

[0023] ,

[0024] in, It is expressed as the fitness value of the individual.

[0025] As a further improvement of this technical solution, the method of sorting individuals according to fitness values ​​in S3 and selecting individuals with high fitness values ​​as parents to perform crossover operations to generate new offspring individuals specifically includes:

[0026] The fitness value of each individual in the statistical genetic algorithm population , sort each individual in the genetic algorithm population from large to small, find two individuals with high fitness values ​​as parents to perform crossover operation, the crossover operation is performed as follows:

[0027] Assume that the modulation signals of the two parent individuals are vectors and vector , randomly select the intersection position as , the two offspring individuals generated after crossover are and .

[0028] As a further improvement of the present technical solution, the method of performing mutation operation on the newly generated offspring individuals in S3 and adding them to the population to replace individuals with low fitness values ​​to form a new population specifically includes:

[0029] For the offspring individual vector and vector , randomly select an element in the vector Perform mutation and change the phase value range Generate a new value randomly Replace the original element value, that is, the individual vector Elements in Updated to , for the vector The same operation is performed on the two mutated offspring individuals. The incident light is modulated using the corresponding modulation signal, and the modulated light is allowed to pass through the scattering medium. The intensity distribution is then measured using a detector, and the contrast value of the image is calculated based on the intensity distribution. This is used as the fitness value of the two offspring individuals. The two mutated offspring individuals are added to the current population, and all individuals in the population are traversed. The individuals are sorted according to their fitness values. If the fitness value of the newly added offspring individual is higher than that of the individual with the lowest fitness value, the individual with the lowest fitness value is replaced with the newly added offspring individual to form a new population.

[0030] As a further improvement of the present technical solution, the method of stopping the iterative optimization in S3 until the maximum contrast value changes less than the contrast value threshold in several consecutive iterations specifically includes:

[0031] Set the contrast value threshold In each iteration, the fitness values ​​of all individuals in the population are calculated, and the maximum contrast value in this iteration is recorded. After each iteration, the maximum contrast value of the current iteration is Maximum contrast value with the previous iteration Compare and calculate the absolute value of the difference between them , If the absolute value of the difference is less than the preset contrast value threshold for 5 consecutive times, , then the stopping condition is considered to be met and the iterative optimization stops.

[0032] As a further improvement of the present technical solution, the verification and evaluation unit evaluates the imaging quality by comparing the peak signal-to-noise ratios of the initial image and the final image, specifically including:

[0033] The sizes of the initial image and the final image obtained by the detector are , the initial image is recorded as , the final image is recorded as , the peak signal-to-noise ratio of the initial image and the final image is obtained by calculating the mean square error between the initial image and the final image, and the calculation formula of the mean square error between the initial image and the final image is:

[0034] ,

[0035] in, Indicates the row index of the initial image and the final image, Indicates the column index of the initial image and the final image, , , Expressed as the mean square error between the initial image and the final image;

[0036] Substitute the mean square error of the initial image and the final image into the formula to calculate the peak signal-to-noise ratio of the initial image and the final image. The specific calculation formula is:

[0037] ,

[0038] in, Expressed as the maximum value of the initial image intensity distribution, find a reference image similar to the initial image from the image database, and calculate the peak signal-to-noise ratio of the initial image and the reference image using the above method ,Will As the peak signal-to-noise ratio of the initial image, if Greater than , it means that the imaging quality of the final image is higher than that of the initial image.

[0039] A second object of the present invention is to provide a method for implementing a quantum imaging system through scattering media based on light modulation and genetic algorithm as described above, comprising the following steps:

[0040] S1. Using a laser to generate incident light, the incident light automatically enters the spatial light modulator, which modulates the incident light according to the modulation signal to form initial modulated light. The initial modulated light automatically passes through the scattering medium to form an initial image.

[0041] S2. Analyzing the intensity distribution of the initial image by calculating a histogram of the initial image to obtain a maximum value and a minimum value of the intensity distribution of the initial image, and calculating a contrast value of the initial image based on the maximum value and the minimum value of the intensity distribution of the initial image, and finally defining an objective function based on the contrast value of the initial image;

[0042] S3. The control center introduces a genetic algorithm to optimize the initial modulation signal according to the objective function. By initializing the genetic algorithm population, individuals of the genetic algorithm population are obtained, and the modulation units corresponding to the individuals of the genetic algorithm population are used to modulate the incident light to obtain an optimized image. The contrast value of the optimized image is used as the fitness value of the individual.

[0043] S4. Calculate the fitness values ​​of all individuals in the genetic algorithm population, and select individuals with high fitness values ​​as parents for crossover operation to generate new offspring individuals. Then perform mutation operation on the newly generated offspring individuals and add them to the population to replace individuals with low fitness values, until the maximum contrast value changes less than the contrast value threshold in several consecutive iterations. Then stop the iterative optimization, and form the modulation units corresponding to the last batch of population individuals into a modulation signal, which is determined to be the modulation signal that maximizes the difference between the bright area and the dark area in the initial image intensity distribution.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] In the quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm, the genetic algorithm can search in the entire search space to avoid falling into the local optimal solution, making it more likely to find the globally optimal modulation signal and improve imaging quality. By optimizing the modulation signal through the genetic algorithm, the difference between the bright and dark areas in the initial image intensity distribution can be maximized, thereby improving the image contrast and making the image clearer. The optimized modulation signal enables the quantum imaging system to obtain better imaging effects when penetrating scattering media, reduce noise and distortion, and improve image quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is an overall block diagram of the present invention;

[0047] Figure 2 It is the overall flow chart of the present invention.

[0048] The meaning of each number in the figure is:

[0049] 1. Generate measurement unit; 2. Optimize shaping unit; 3. Verify evaluation unit. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Example

[0051] The present invention provides a quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm. Figure 1 As shown, it includes a generation and measurement unit 1, an optimization and shaping unit 2, and a verification and evaluation unit 3;

[0052] The generation and measurement unit 1 sends the incident light generated by the laser to the spatial light modulator. The spatial light modulator modulates the incident light according to the initial modulation signal sent by the control center to form an initial modulated light. The detector measures the initial modulated light to obtain the size and pixel intensity value of the initial image.

[0053] The spatial light modulator in the measurement unit 1 modulates the incident light according to the modulation signal sent by the control center to form the modulated light, specifically including:

[0054] In quantum imaging systems, the incident light needs to be modulated to obtain the required initial modulated light. By utilizing the birefringence characteristics of the liquid crystal spatial light modulator, the phase of the light can be effectively controlled, thereby achieving light modulation.

[0055] The spatial light modulator is a liquid crystal spatial light modulator. The liquid crystal spatial light modulator uses the birefringence characteristics of liquid crystal molecules to control the phase of the incident light. When the arrangement of liquid crystal molecules changes under the action of different electric fields, their refractive index will also change accordingly, causing the phase of the incident light to change when passing through the liquid crystal layer, forming modulated light. Precise phase modulation helps to better control the propagation and interference of light, thereby improving the quality and clarity of imaging, and providing a suitable foundation for subsequent image measurement, analysis and optimization.

[0056] For example, for a specific pixel position, the modulation signal sent by the control center determines the orientation and electric field strength of the liquid crystal molecules at that pixel. According to the optical properties of liquid crystal, the phase delay of the incident light when passing through the pixel can be expressed as:

[0057] ,

[0058] in, represents the pixel position, is the wavelength of the incident light, is the thickness of the liquid crystal layer, is the change in the birefringence of the liquid crystal at the pixel position. The control center changes the birefringence of each pixel by Achieve precise modulation of the phase of incident light.

[0059] In quantum imaging systems, the initial image is often affected by the scattering medium, resulting in unclear differences between bright and dark areas and reduced imaging quality. By measuring and analyzing the initial image, obtaining its intensity distribution, and feeding back the initial modulation signal, the control center can use genetic algorithms to optimize the modulation signal to find a modulation signal that maximizes the difference between bright and dark areas, thereby improving imaging quality.

[0060] The optimization shaping unit 2 obtains the intensity distribution of the initial image based on the size and pixel intensity value of the initial image, and feeds back the initial modulation signal corresponding to the initial image to the control center. The control center introduces a genetic algorithm to iteratively update and optimize the initial modulation signal until a modulation signal is obtained that maximizes the difference between the bright and dark areas in the intensity distribution of the initial image.

[0061] The steps for optimizing the operation of shaping unit 2 are as follows:

[0062] Directly optimizing the intensity distribution of the initial image can more accurately find the direction to improve the imaging quality;

[0063] A1. Analyze the intensity distribution of the initial image based on the size and pixel value intensity of the initial image to obtain the maximum and minimum values ​​of the initial image intensity distribution. Calculate the contrast value of the initial image based on the maximum and minimum values ​​of the initial image intensity distribution. Then, define an objective function based on the contrast value of the initial image. The objective function is a modulation signal that maximizes the difference between bright and dark areas in the initial image intensity distribution. This objective function is fed back to the control center to maximize the difference between bright and dark areas in the initial image intensity distribution, thereby significantly improving the contrast of the image and making the image clearer and more discernible.

[0064] A2. The control center uses a genetic algorithm to optimize the initial modulation signal. Specifically, a group of initial modulation signals of spatial light modulators are randomly selected to form a genetic algorithm population. Each modulation unit of the initial modulation signal represents an individual in the genetic algorithm population. For each individual in the genetic algorithm population, the incident light is modulated using its corresponding modulation unit to obtain optimized modulated light. The optimized modulated light is then passed through a scattering medium to form an optimized image. The intensity distribution of the optimized image is measured using a detector. Finally, the contrast value of the optimized image is calculated based on the intensity distribution of the optimized image, and the contrast value of the optimized image is used as the fitness value of the individual. The genetic algorithm can automatically perform iterative updates and optimizations, reducing manual intervention and improving efficiency. The optimized modulation signal can better penetrate the scattering medium, reduce noise and distortion, and improve the overall image quality.

[0065] A3. Sort individuals according to their fitness values, and select individuals with high fitness values ​​as parents for crossover operations to generate new offspring individuals. Then, perform mutation operations on the newly generated offspring individuals and add them to the population, replacing individuals with low fitness values ​​to form a new population. The next iteration is performed until the maximum contrast value changes less than the contrast value threshold in several consecutive iterations. The iterative optimization is stopped. The genetic algorithm can find the optimal solution in a large search space and avoid falling into local optimality. It can automatically adjust the modulation signal according to different initial images and scattering media conditions to obtain the best imaging effect.

[0066] The method of calculating the contrast value of the initial image according to the maximum and minimum values ​​of the intensity distribution of the initial image in A1 specifically includes:

[0067] The histogram can intuitively display the distribution of pixels of different intensity levels in the image. By analyzing the histogram, the grayscale distribution characteristics of the image can be quickly obtained, including information such as the maximum and minimum values, thus providing a basis for calculating the contrast value.

[0068] Assume that the initial image has L different intensity levels, and the intensity levels are arrive , the size of the initial image measured by the detector is , the intensity value of each pixel is ,in, , , the intensity value of each pixel is Substitute the formula to calculate the histogram of the initial image , the specific calculation formula is:

[0069] ,

[0070] in, , is the histogram of the initial image function, when hour, , otherwise it is 0. By traversing and counting the histogram, the maximum and minimum values ​​can be quickly calculated, and then the contrast value can be calculated. The calculation efficiency is high. Traversing the histogram of the initial image , find the first non-zero The value is the minimum value , find the last non-zero value, which is the maximum value ,The maximum and minimum values ​​calculated based on the histogram can accurately reflect the intensity range of the bright and dark areas in the image, so that the calculated contrast value can truly reflect the contrast of the image;

[0071] The maximum value of the initial image intensity distribution and minimum value Substitute the formula to calculate the contrast value of the initial image , the specific calculation formula is:

[0072] ,

[0073] The contrast value of the initial image The contrast value parameter used to maximize the difference between the bright and dark areas in the initial image is used as the objective function.

[0074] Genetic algorithms are random search algorithms that mimic natural evolutionary processes and are capable of finding optimal solutions in complex search spaces. In quantum imaging systems, optimizing the initial modulation signal is a complex problem, requiring the search for a modulation signal that maximizes the difference between bright and dark regions in the initial image intensity distribution among a large number of possible solutions. Genetic algorithms, with their global search capabilities and parallelism, are able to effectively handle such complex optimization problems.

[0075] In A2, the control center introduces a genetic algorithm to optimize the modulation signal, specifically including:

[0076] The number of pixels of the spatial light modulator is recorded as , where each pixel can control the phase of the incident light. Now the modulation signal emitted by the control center is set to a length of A vector where each element represents the phase value of a pixel, and the phase value range is set to , randomly selected according to the phase value range The different lengths are As the initial population of the genetic algorithm, for the first individual in the initial population, let its modulation signal be vector ,in It is represented as the phase value of the i-th pixel. The spatial light modulator adjusts the phase of the incident light according to these phase values ​​to obtain the optimized modulated light. The contrast value of the image is calculated according to the intensity distribution of the optimized modulated light and used as the fitness value of the individual. The specific calculation formula is:

[0077] ,

[0078] in, It is expressed as the fitness value of the individual. The higher the contrast, that is, the greater the difference between the bright area and the dark area, the higher the fitness value.

[0079] The core idea of ​​genetic algorithms is to find the optimal solution by simulating the natural evolutionary process. In this process, selection, crossover, and mutation are key steps, working together to search and optimize the solution space. By sorting individuals, selecting individuals with high fitness for crossover, and mutating newly generated offspring individuals, the modulation signal can be gradually optimized, evolving toward maximizing the difference between bright and dark areas in the initial image intensity distribution. After multiple iterations of optimization, a more optimal modulation signal can be found, thereby improving the contrast of the initial image and maximizing the difference between bright and dark areas.

[0080] In A3, individuals are sorted according to their fitness values, and individuals with high fitness values ​​are selected as parents for crossover operations to generate new offspring individuals. Specifically, the following methods are used:

[0081] The fitness value of each individual in the statistical genetic algorithm population , sort each individual in the genetic algorithm population from large to small, find two individuals with high fitness values ​​as parents for crossover operation, and the crossover operation is performed as follows:

[0082] Assume that the modulation signals of the two parent individuals are vectors and vector , randomly select the intersection position as , the two offspring individuals generated after crossover are and By exchanging some genes of the two parent individuals after the crossover point, gene recombination and inheritance are achieved, the diversity of the population is increased, and it helps the algorithm explore more possibilities in the search space and avoid falling into the local optimal solution too early.

[0083] In A3, the newly generated offspring individuals are mutated and added to the population to replace individuals with low fitness values ​​to form a new population. Specifically, the following methods are used:

[0084] For the offspring individual vector and vector , randomly select an element in the vector Perform mutation and change the phase value range Generate a new value randomly Replace the original element value, that is, the individual vector Elements in Updated to , for the vector The same operation is performed on the two mutated offspring individuals, and their corresponding modulation signals are used to modulate the incident light. The modulated light is allowed to pass through the scattering medium, and then the intensity distribution is measured using a detector. The contrast value of the image is calculated based on the intensity distribution as the fitness value of the two offspring individuals. The two mutated offspring individuals are added to the current population, and all individuals in the population are traversed and sorted according to the fitness value. If the fitness value of the newly added offspring individual is higher than that of the individual with the lowest fitness value, the individual with the lowest fitness value is replaced by the newly added offspring individual to form a new population, which increases the diversity of the population. By continuously mutating and updating the population, the algorithm can gradually find offspring individuals with higher fitness, thereby improving the quality of the final modulation signal, making the image contrast higher and the imaging quality better.

[0085] The iterative optimization process of a genetic algorithm continuously searches for the optimal solution. However, in practice, due to computational resource and time constraints, it is impossible to iterate indefinitely. Therefore, a stopping condition is required to determine when to stop iteration and obtain a satisfactory solution. By comparing the difference between the maximum contrast value of the current iteration and the maximum contrast value of the previous iteration, it can be determined whether the algorithm has converged to a relatively stable state. If the absolute value of the difference is less than the preset contrast value threshold for multiple consecutive times, it indicates that the algorithm is no longer able to optimize further. Stopping the iteration at this point can save computational resources.

[0086] In A3, the iterative optimization method is stopped until the maximum contrast value changes less than the contrast value threshold in several consecutive iterations. Specifically, the method includes:

[0087] Set the contrast value threshold In each iteration, the fitness values ​​of all individuals in the population are calculated, and the maximum contrast value in this iteration is recorded. After each iteration, the maximum contrast value of the current iteration is Maximum contrast value with the previous iteration Compare and calculate the absolute value of the difference between them , If the absolute value of the difference is less than the preset contrast value threshold for 5 consecutive times , then the stopping condition is considered to be met and the iterative optimization stops. Within a reasonable time, the algorithm can find a relatively optimal modulation signal to maximize the difference between the bright and dark areas in the initial image intensity distribution and improve the imaging quality.

[0088] The verification and evaluation unit 3 sends the modulation signal to the spatial light modulator through the control center. The spatial light modulator modulates the incident light according to the modulation signal to form modulated light. The modulated light passes through the scattering medium to form the final image. Finally, the imaging quality is evaluated by comparing the peak signal-to-noise ratio of the initial image and the final image.

[0089] In order to evaluate the imaging quality of the quantum imaging system after optimization and shaping, it is necessary to compare the initial image and the final image after modulated signal processing. Peak signal-to-noise ratio is a commonly used image quality evaluation indicator that can quantitatively reflect the difference between images.

[0090] The verification and evaluation unit 3 evaluates the imaging quality by comparing the peak signal-to-noise ratios of the initial image and the final image, specifically including:

[0091] The sizes of the initial image and the final image obtained by the detector are , the initial image is recorded as , the final image is recorded as , the peak signal-to-noise ratio of the initial image and the final image is obtained by calculating the mean square error between the initial image and the final image. The calculation formula of the mean square error between the initial image and the final image is:

[0092] ,

[0093] in, Indicates the row index of the initial image and the final image, Indicates the column index of the initial image and the final image, , , Expressed as the mean square error between the initial image and the final image;

[0094] Substitute the mean square error of the initial image and the final image into the formula to calculate the peak signal-to-noise ratio of the initial image and the final image. The specific calculation formula is:

[0095] ,

[0096] in, Expressed as the maximum value of the initial image intensity distribution, find a reference image similar to the initial image from the image database, and calculate the peak signal-to-noise ratio of the initial image and the reference image using the above method ,Will As the peak signal-to-noise ratio of the initial image, if Greater than , it means that the imaging quality of the final image is higher than that of the initial image.

[0097] In the present invention, a spatial light modulator is modulated by a modulation signal to obtain initial modulated light, and the initial modulated light is passed through a scattering medium to form an initial image. The maximum and minimum values ​​of the initial image intensity distribution are obtained from the histogram of the initial image, and the contrast of the initial image is calculated based on the maximum and minimum values ​​of the initial image intensity distribution. Then, an objective function is defined based on the contrast value of the initial image. A control center introduces a genetic algorithm to optimize the initial modulation signal based on the objective function, finds the modulation signal that maximizes the difference between the bright area and the dark area in the intensity distribution of the initial image, and calculates the peak signal-to-noise ratio of the final image generated by the modulation signal to evaluate the imaging quality. Example

[0098] A second object of the present invention is to provide a method for implementing any one of the above-mentioned quantum imaging systems for penetrating scattering media based on light modulation and genetic algorithms, comprising the following steps:

[0099] S1. Using a laser to generate incident light, the incident light automatically enters the spatial light modulator, which modulates the incident light according to the modulation signal to form initial modulated light. The initial modulated light automatically passes through the scattering medium to form an initial image.

[0100] S2. Analyzing the intensity distribution of the initial image by calculating a histogram of the initial image to obtain a maximum value and a minimum value of the intensity distribution of the initial image, and calculating a contrast value of the initial image based on the maximum value and the minimum value of the intensity distribution of the initial image, and finally defining an objective function based on the contrast value of the initial image;

[0101] S3. The control center introduces a genetic algorithm to optimize the initial modulation signal according to the objective function. By initializing the genetic algorithm population, individuals of the genetic algorithm population are obtained, and the modulation units corresponding to the individuals of the genetic algorithm population are used to modulate the incident light to obtain an optimized image. The contrast value of the optimized image is used as the fitness value of the individual.

[0102] S4. Calculate the fitness values ​​of all individuals in the genetic algorithm population, and select individuals with high fitness values ​​as parents for crossover operation to generate new offspring individuals. Then perform mutation operation on the newly generated offspring individuals and add them to the population to replace individuals with low fitness values, until the maximum contrast value changes less than the contrast value threshold in several consecutive iterations. Then stop the iterative optimization, and form the modulation units corresponding to the last batch of population individuals into a modulation signal, which is determined to be the modulation signal that maximizes the difference between the bright area and the dark area in the initial image intensity distribution.

[0103] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm, characterized by: It includes a measurement generation unit (1), an optimization shaping unit (2), and a verification and evaluation unit (3); The generating and measuring unit (1) sends the incident light generated by the laser to the spatial light modulator, and the spatial light modulator modulates the incident light according to the initial modulation signal sent by the control center to form an initial modulated light, and uses a detector to measure the initial modulated light to obtain the size and pixel intensity value of the initial image; The optimization shaping unit (2) obtains the intensity distribution of the initial image according to the size and pixel intensity value of the initial image, and feeds back the initial modulation signal corresponding to the initial image to the control center, and the control center introduces a genetic algorithm to iteratively update and optimize the initial modulation signal until a modulation signal that maximizes the difference between the bright area and the dark area in the intensity distribution of the initial image is obtained; The verification and evaluation unit (3) sends a modulation signal to a spatial light modulator through a control center. The spatial light modulator modulates the incident light according to the modulation signal to form a modulated light. The modulated light passes through a scattering medium to form a final image. Finally, the imaging quality is evaluated by analyzing the peak signal-to-noise ratio of the initial image and the final image.

2. The quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm according to claim 1, characterized in that: The spatial light modulator in the generating and measuring unit (1) modulates the incident light according to the initial modulation signal sent by the control center to form the initial modulated light, specifically including: The spatial light modulator is a liquid crystal spatial light modulator, which uses the birefringence characteristics of liquid crystal molecules to control the phase of incident light. When the arrangement of the liquid crystal molecules changes under the action of different electric fields, their refractive index will also change accordingly, causing the phase of the incident light to change when passing through the liquid crystal layer, forming initial modulated light.

3. The quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm according to claim 1, characterized in that: The steps of running the optimization shaping unit (2) are as follows: A1. Analyze the intensity distribution of the initial image based on the size and pixel value intensity of the initial image to obtain the maximum and minimum values ​​of the initial image intensity distribution, and calculate the contrast value of the initial image based on the maximum and minimum values ​​of the initial image intensity distribution. Then, define an objective function based on the contrast value of the initial image and feed it back to the control center. The objective function is the modulation signal corresponding to the maximum contrast that maximizes the difference between bright and dark areas in the intensity distribution of the initial image. A2. The control center introduces a genetic algorithm to optimize the initial modulation signal. Specifically, the control center randomly extracts the initial modulation signals of a group of spatial light modulators to form a genetic algorithm population. Each modulation unit in the spatial light modulator represents an individual in the genetic algorithm population. For each genetic algorithm population individual, the corresponding modulation unit is used to modulate the incident light to obtain optimized modulated light. The optimized modulated light is then passed through a scattering medium to form an optimized image. The intensity distribution of the optimized image is measured using a detector. Finally, the contrast value of the optimized image is calculated based on the intensity distribution of the optimized image, and the contrast value of the optimized image is used as the fitness value of the individual in the genetic algorithm population. A3. Sort the genetic algorithm population individuals according to their fitness values, and select the genetic algorithm population individuals with high fitness values ​​as parents for crossover operation to generate new offspring individuals. Then perform mutation operation on the new offspring individuals, add the mutated sub-band individuals to the population to replace the genetic algorithm population individuals with low fitness values ​​to form a new population, and perform the next iteration until the maximum contrast value changes less than the contrast value threshold in several consecutive iterations, then stop the iterative optimization.

4. The quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm according to claim 3, characterized in that: The method of calculating the contrast value of the initial image according to the maximum and minimum values ​​of the intensity distribution of the initial image in A1 specifically includes: Assume that the initial image has L different intensity levels, the intensity levels are arrive , the size of the initial image measured by the detector is , the intensity value of each pixel is ,in, , , substitute the intensity value of each pixel into the formula to calculate the histogram of the initial image , the specific calculation formula is: ， in, , is the initial image histogram function, when hour, , otherwise 0, traverse the initial image histogram , find the first non-zero Value as minimum , find the last non-zero Value as maximum value , and the maximum value of the initial image intensity distribution and minimum value Substitute the formula to calculate the contrast value of the initial image , the specific calculation formula is: ， The contrast value of the initial image It is used to represent the degree of difference between the bright area and the dark area in the initial image. The modulation signal corresponding to the maximum contrast value that maximizes the difference between the bright area and the dark area in the intensity distribution of the initial image is defined as the objective function.

5. The quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm according to claim 4, characterized in that: The method in A2 where the control center introduces a genetic algorithm to optimize the initial modulation signal specifically includes: The number of pixels of the spatial light modulator is recorded as , where each pixel can control the phase of the incident light, and then the initial modulation signal sent by the control center is set to a length of A vector where each element represents the phase value of a pixel, and the phase value range is set to , randomly selected according to the phase value range The different lengths are As the initial population of the genetic algorithm, for the first individual in the initial population, let its modulation signal be vector ,in The spatial light modulator adjusts the phase of the incident light according to the phase value to obtain the optimized modulated light. The contrast value of the optimized image is calculated according to the intensity distribution of the optimized modulated light, and the contrast value of the optimized image is used as the fitness value of the individual. The specific calculation formula is: ， in, It is expressed as the fitness value of the individual.

6. The quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm according to claim 5, characterized in that: The method of sorting the genetic algorithm population individuals according to their fitness values ​​in A3, and selecting individuals with high fitness values ​​as parents to perform crossover operations to generate new offspring individuals specifically includes: The fitness value of each individual in the statistical genetic algorithm population , sort each individual in the genetic algorithm population from large to small, find two individuals with high fitness values ​​as parents to perform crossover operation, the crossover operation is performed as follows: Assume that the modulation signals of the two parent individuals are vectors and vector , randomly select the intersection point , the two offspring individuals generated after crossover are and .

7. The quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm according to claim 6, characterized in that: In A3, the newly generated offspring individuals are mutated, and the mutated sub-band individuals are added to the population to replace the genetic algorithm population individuals with low fitness values ​​to form a new population. Specifically, the method includes: For the offspring individual vector and vector , randomly select an element in the vector Perform mutation and change the phase value range Generate a new value randomly Replace the original element value, that is, the individual vector Elements in Updated to , for the vector The same operation is performed on the two mutated offspring individuals. The incident light is modulated using their corresponding modulation signals, and the modulated light is allowed to pass through the scattering medium. The intensity distribution is then measured using a detector, and the contrast value of the image is calculated based on the intensity distribution. This is used as the fitness value of the two offspring individuals. The two mutated offspring individuals are added to the current population, and all individuals in the population are traversed. The individuals are sorted according to their fitness values. If the fitness value of the newly added offspring individual is higher than that of the individual with the lowest fitness value, the individual with the lowest fitness value is replaced with the newly added offspring individual to form a new population.

8. The quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm according to claim 6, characterized in that: The method of stopping the iterative optimization in A3 until the maximum contrast value changes less than the contrast value threshold in several consecutive iterations specifically includes: Set the contrast value threshold In each iteration, the fitness values ​​of all individuals in the population are calculated, and the maximum contrast value in this iteration is recorded. After each iteration, the maximum contrast value of the current iteration is Maximum contrast value with the previous iteration Compare and calculate the absolute value of the difference between them , If the absolute value of the difference is less than the preset contrast value threshold for 5 consecutive times, , then the stopping condition is considered to be met, and the iterative optimization is stopped, and the modulation signal corresponding to the last batch of population individuals is set to the modulation signal that maximizes the difference between the bright area and the dark area in the initial image intensity distribution.

9. The quantum imaging system for penetrating scattering media based on light modulation and genetic algorithm according to claim 1, characterized in that: The verification and evaluation unit (3) evaluates the imaging quality by comparing the peak signal-to-noise ratio of the initial image and the final image, specifically including: The sizes of the initial image and the final image obtained by the detector are , the initial image is recorded as , the final image is recorded as , the peak signal-to-noise ratio of the initial image and the final image is obtained by calculating the mean square error between the initial image and the final image, and the calculation formula of the mean square error between the initial image and the final image is: ， in, Indicates the row index of the initial image and the final image, Indicates the column index of the initial image and the final image, , , Expressed as the mean square error between the initial image and the final image; Substitute the mean square error of the initial image and the final image into the formula to calculate the peak signal-to-noise ratio of the initial image and the final image. The specific calculation formula is: ， in, Expressed as the maximum value of the initial image intensity distribution, find a reference image similar to the initial image from the image database, and calculate the peak signal-to-noise ratio of the initial image and the reference image using the above formula ,Will As the peak signal-to-noise ratio of the initial image, if Greater than , it means that the imaging quality of the final image is higher than that of the initial image.

10. A method for implementing a quantum imaging system through scattering media based on light modulation and genetic algorithm according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Using a laser to generate incident light, the incident light automatically enters the spatial light modulator, which modulates the incident light according to the modulation signal to form initial modulated light. The initial modulated light automatically passes through the scattering medium to form an initial image. S2. Analyzing the intensity distribution of the initial image by calculating a histogram of the initial image to obtain a maximum value and a minimum value of the intensity distribution of the initial image, and calculating a contrast value of the initial image based on the maximum value and the minimum value of the intensity distribution of the initial image, and finally defining an objective function based on the contrast value of the initial image; S3. The control center introduces a genetic algorithm to optimize the initial modulation signal according to the objective function. By initializing the genetic algorithm population, individuals of the genetic algorithm population are obtained, and the modulation units corresponding to the individuals of the genetic algorithm population are used to modulate the incident light to obtain an optimized image. The contrast value of the optimized image is used as the fitness value of the individual. S4. Calculate the fitness values ​​of all individuals in the genetic algorithm population, and select individuals with high fitness values ​​as parents for crossover operation to generate new offspring individuals. Then perform mutation operation on the newly generated offspring individuals and add them to the population to replace individuals with low fitness values, until the maximum contrast value changes less than the contrast value threshold in several consecutive iterations. Then stop the iterative optimization, and form the modulation units corresponding to the last batch of population individuals into a modulation signal, which is determined to be the modulation signal that maximizes the difference between the bright area and the dark area in the initial image intensity distribution.

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