Non-invasive imaging method and device through dynamic scattering medium

Abstract

The invention belongs to the technical field of optical imaging, and discloses a non-intrusive imaging method and device penetrating through a dynamic scattering medium, and the method comprises the steps: controlling a preset coherent object light beam to irradiate an object to be imaged, and forming an object light field; collecting a speckle intensity image of a speckle field formed by an object light field penetrating through the preset dynamic scattering medium in a far-field region, wherein the far-field region is a region of the preset dynamic scattering medium penetrated by the object light field; based on a four-moment correlation function of the speckle intensity image, analyzing to obtain a far-field mutual dry function; according to a function relationship between the far-field mutual coherence function and the object light field autocorrelation function, analyzing to obtain an autocorrelation function of the object to be imaged; and based on a phase recovery algorithm, reconstructing an image of the to-be-imaged object through the autocorrelation function. According to the invention, view field limitation caused by dependence on an optical memory effect is avoided, imaging can be completed through single exposure, a data acquisition process is simplified, and adaptability to a dynamic scattering medium and imaging efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of optical imaging technology, and particularly relates to a non-invasive imaging method and device for penetrating dynamic scattering medium. BACKGROUND

[0002] Optical imaging technology has important applications in the fields of biomedical science, optical detection and astronomy. However, non-uniform media such as biological tissues will scatter light beams, so that the light beams form complex speckle patterns, thereby hindering the direct acquisition of object images. In order to reconstruct the object image information carried by the light beams from the speckle patterns, the prior art has proposed various methods, such as wavefront modulation technology, transmission matrix method and speckle correlation imaging.

[0003] Among them, the speckle autocorrelation imaging (SACI) method uses the spatial translation invariance of the point spread function to recover the object image through the autocorrelation of a single speckle intensity image, and has a faster imaging speed. However, this method relies on the optical memory effect (OME), and the imaging field of view is strictly limited to the OME region range (usually in the order of microns), and cannot recover the image of a large-size object. In addition, although the moving object speckle correlation imaging (SCIMO) method can extend the field of view by collecting multiple speckle images of the object at different positions, it needs to move the object multiple times and take multiple speckle patterns. Under the condition of dynamic scattering medium (such as living tissue), since the medium itself changes rapidly with time, it is difficult to stably acquire multiple speckle images, so it is difficult to realize effective imaging.

[0004] Therefore, there is an urgent need in the art for a non-invasive imaging method and device for penetrating dynamic scattering medium to solve the above technical problems. SUMMARY

[0005] Therefore, the present application provides a non-invasive imaging method and device for penetrating dynamic scattering medium, which solves the problems of small imaging field of view, dependence on multiple speckle image acquisition and inability to be applied to dynamic scattering medium in the prior art.

[0006] In order to solve the above technical problems, the present application provides a non-invasive imaging method for penetrating dynamic scattering medium, comprising: controlling a pre-set coherent object light beam to irradiate a to-be-imaged object, to form an object light field; acquiring a speckle intensity image of a speckle field formed by the object light field penetrating the pre-set dynamic scattering medium in a far-field region, wherein the far-field region is a region of the pre-set dynamic scattering medium penetrated by the object light field; analyzing a far-field mutual coherence function based on a fourth-order moment correlation function of the speckle intensity image; analyzing a self-correlation function of the to-be-imaged object according to a functional relationship between the far-field mutual coherence function and a self-correlation function of the object light field. reconstructing an image of the object to be imaged based on the autocorrelation function and a phase retrieval algorithm.

[0007] As a further improvement of the application, the autocorrelation function of the object to be imaged is obtained by analyzing the function relationship between the far-field mutual coherence function and the autocorrelation function of the object light field, comprising: obtaining a modulus value of the far-field mutual coherence function when the first preset observation point coordinate and the second preset observation point coordinate are equal; eliminating the Gaussian envelope caused by the coherence length of the scattering medium in the modulus value to obtain a processed function; performing inverse Fourier transform on the processed function to obtain the autocorrelation function of the object to be imaged.

[0008] As a further improvement of the application, the processed function is obtained by the following formula: The non-invasive imaging method through a dynamic scattering medium according to claim 2, wherein the processed function is obtained by the following formula: ; wherein, is a modulus square value of the far-field mutual coherence function; is a coherence length of the preset dynamic scattering medium; is a spatial coordinate difference of the near-field plane; is a modulus square value of the Fourier transform of the autocorrelation function of the object to be imaged.

[0009] As a further improvement of the application, the image of the object to be imaged is reconstructed based on the autocorrelation function and a phase retrieval algorithm, comprising: performing Fourier transform on the autocorrelation function to obtain Fourier amplitude information of the object to be imaged; based on the Fourier amplitude information, combining a preset real space constraint condition, and solving Fourier phase information of the object to be imaged by an iterative phase retrieval algorithm; combining the Fourier amplitude information and the Fourier phase information, and performing inverse Fourier transform on the combined Fourier amplitude information and the Fourier phase information to reconstruct a spatial distribution image of the object to be imaged.

[0010] As a further improvement of the application, the iterative phase retrieval algorithm is a hybrid input-output algorithm or an error reduction algorithm.

[0011] As a further improvement of the application, the preset real space constraint condition at least includes one of the following: The spatial distribution of the object to be imaged is non-negative; The spatial distribution of the object to be imaged has a finite support domain; The spatial distribution of the object to be imaged has real value properties.

[0012] As a further improvement of the present invention, the step of solving the Fourier phase of the object to be imaged using an iterative phase retrieval algorithm based on the Fourier amplitude information and in combination with preset real space constraints includes: The current Fourier amplitude to be iterated is combined with the preset guessed phase and then inverse Fourier transform is performed to obtain the candidate image in real space. The real space constraint is applied to the candidate image to obtain the updated image; Perform a Fourier transform on the updated image to obtain the updated frequency domain data; The phase in the updated frequency domain data is retained, and the amplitude of the updated frequency domain data is replaced with the Fourier amplitude information to form the iterated frequency domain input; The frequency domain input is continuously iterated to form new inputs until the preset convergence condition is met or the maximum number of iterations is reached. The phase in the finally updated frequency domain data is used as the Fourier phase of the object to be imaged.

[0013] The present invention also provides a non-invasive imaging device that transmits through a dynamic scattering medium, comprising: A light source used to emit a beam of light; A spatial light modulator is used to modulate the light beam to form an object light field; A scattering medium is placed in the propagation path of the object's light field to scatter the object's light field and form a speckle field. An image sensor, disposed in the far-field region of the scattering medium, is used to acquire a single speckle intensity image; The data processing module is used to process the speckle intensity image to reconstruct an object image, wherein the processing includes calculating the far-field mutual coherence function, deriving the object autocorrelation function, and reconstructing the object image using a phase retrieval algorithm.

[0014] As a further improvement of the present invention, a 4f optical component, including a lens group and a pinhole, is provided between the spatial light modulator and the scattering medium for expanding, collimating and filtering the light beam.

[0015] As a further improvement of the present invention, the data processing module is configured to perform the following operations: Perform correlation calculations on the speckle intensity image; The relevant results are locally scaled and Fourier transformed; the phase retrieval algorithm is applied to output the object image.

[0016] Compared with existing technologies, this invention provides a non-invasive imaging method and apparatus for dynamic scattering media. The method utilizes a highly coherent light source to form an object light field. After acquiring a single speckle intensity image in the far-field region, the far-field mutual coherence function is obtained based on the fourth-order moment correlation function, and the object's autocorrelation function is derived. Finally, the object image is reconstructed using a phase retrieval algorithm. Compared to existing speckle autocorrelation imaging methods, which are limited by the optical memory effect region resulting in a small imaging field of view, and moving object speckle autocorrelation imaging methods, which require multiple object movements and acquisition of multiple speckle images and are unsuitable for dynamic scattering media, this invention avoids the field-of-view limitations caused by reliance on the optical memory effect, achieving a large field of view imaging far exceeding the OME range. It also avoids the experimental infeasibility caused by the need to acquire multiple speckle images in dynamic scattering environments, as imaging can be completed with a single exposure, significantly simplifying the data acquisition process and improving adaptability to dynamic scattering media and imaging efficiency. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and not all embodiments. For those skilled in the art, other drawings obtained from these drawings without creative effort are all within the scope of protection of this application.

[0018] Figure 1 This is a flowchart of a non-invasive imaging method through a dynamic scattering medium provided in an embodiment of the present invention.

[0019] Figure 2 yes Figure 1 A flowchart of step S104.

[0020] Figure 3 yes Figure 1 A flowchart of step S105.

[0021] Figure 4 yes Figure 3 A flowchart of step S1052.

[0022] Figure 5 These are experimental results of the non-invasive imaging method and apparatus through a dynamic scattering medium provided in the embodiments of the present invention: (a) speckle intensity image, (b) modulus of far-field MCF, (c) calculated from the speckle intensity image according to equation (4), (d) reconstructed object, (e) hidden object, and (f) autocorrelation of the object image.

[0023] Figure 6 This is a perspective view of the non-invasive imaging device that transmits through a dynamic scattering medium, as provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0025] To make the description of this disclosure more detailed and complete, illustrative descriptions of embodiments and specific examples of the present invention are provided below; however, these are not the only forms of implementing or utilizing the specific embodiments of the present invention. The embodiments cover features of multiple specific embodiments and the methods, steps, and their order for constructing and operating these specific embodiments. However, other specific embodiments may also be used to achieve the same or equivalent functions and step sequences. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0028] In the description of the embodiments of the present invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The word "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more. Other quantifiers should be understood similarly. The preferred embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0029] Please refer to Figures 1-6 This invention provides a non-invasive imaging method and apparatus that transmits images through a dynamic scattering medium, thereby solving the problems of small imaging field of view, reliance on multiple speckle images, and inapplicability to dynamic scattering media in existing technologies.

[0030] Specifically, please refer to Figure 1 A flowchart of a non-invasive imaging method through a dynamically scattering medium provided by the present invention is provided, the non-invasive imaging method through a dynamically scattering medium comprising: Step S101: Control the preset coherent object beam to illuminate the object to be imaged, forming an object beam field.

[0031] In this embodiment, a laser is used as the light source to generate a highly coherent beam. This beam passes through an intensity modulation system consisting of a half-wave plate (HWP) and a polarization beam splitter (PBS), and then undergoes beam expansion, collimation, and spatial filtering via a 4f optical system (including lenses L1, L2, and pinhole P) to obtain a high-quality collimated beam. Subsequently, this beam illuminates a spatial light modulator (SLM). By loading a specially designed hologram (e.g., using a checkerboard pattern) onto the SLM, the complex amplitude of the beam can be modulated, thereby generating an object light field with an intensity distribution of a specific shape (in this embodiment, an "S" shape). This object light field is equivalent to the illuminated object to be imaged.

[0032] Step S102: Acquire a speckle intensity image of the speckle field formed by the object light field passing through the preset dynamic scattering medium in the far field region.

[0033] The resulting beam then penetrates a piece of fresh chicken breast (approximately 2 mm thick) serving as a dynamic scattering medium. After passing through the scattering medium, the beam is scattered, forming a random speckle field. A charge-coupled device (CCD) image sensor is placed 300 mm from the back surface of the scattering medium, a location defined as the far-field region. Within this far-field region, the CCD acquires a single image of the speckle intensity of the speckle field. The effective pixel array size of the CCD is 1392×1040, and the pixel size is 6.45. × 6.45 Experimental measurements show that the contrast of all acquired speckle intensity images is close to 1, indicating that the speckle field at the detector is fully developed and meets the statistical conditions for subsequent processing. A single acquired speckle intensity image is shown below. Figure 5 As shown in (a).

[0034] Step S103: Based on the fourth-order moment correlation function of the speckle intensity image, the far-field mutual coherence function is obtained through analysis.

[0035] For a fully developed speckle pattern, its statistical properties follow a zero-mean circular Gaussian distribution. According to the statistical theory of circular Gaussian fields, there is a definite relationship between the fourth and second moments of the field. Specifically, two points in the far field... and Correlation of speckle intensity fluctuations The squared magnitude of the modulus of the fourth-order moment correlation function and the far-field coherence function (MCF) at these two points. Proportional.

[0036] The relationship is given by the following formula: in, , It represents the spatial average.

[0037] Therefore, by calculating the intensity of a single speckle image acquired... The fourth-order moment correlation function can be used to directly obtain the squared magnitude of the far-field coherence function. The calculation obtained in this embodiment The distribution is as follows Figure 5 As shown in (b).

[0038] Step S104: Based on the functional relationship between the far-field mutual coherence function and the object field autocorrelation function, the autocorrelation function of the object to be imaged is obtained.

[0039] This step aims to extract the autocorrelation function of the object to be imaged from the far-field MCF. Specifically, it includes: Obtain the coordinates of the far-field coherence function at the observation point. Modulus squared value This corresponds to the data on the diagonal in the result of step S103.

[0040] Based on theoretical derivation, Fourier transform and autocorrelation function of incident light field The squared modulus of the Fourier transform There is a functional relationship between them, but it includes a factor determined by the coherence length of the scattering medium. Gaussian envelope caused .

[0041] The influence of the Gaussian envelope is eliminated through mathematical processing (such as deconvolution or fitting elimination in the frequency domain), thereby obtaining... .

[0042] right By performing an inverse Fourier transform, the autocorrelation function of the object to be imaged can be obtained. In this embodiment, the object autocorrelation function derived through this process is as follows: Figure 5 As shown in (f).

[0043] Step S105: Based on the phase retrieval algorithm, reconstruct the image of the object to be imaged using the autocorrelation function.

[0044] Obtain the autocorrelation function of the object Then, according to the autocorrelation theorem, its Fourier transform is the intensity distribution of the object. The power spectrum (i.e., the square of the Fourier amplitude) is required. However, Fourier phase information is also needed to reconstruct the image.

[0045] First, regarding the autocorrelation function Perform a Fourier transform and take the square root to obtain the amplitude information of the object's Fourier transform. .

[0046] Then, using a phase recovery algorithm (in this embodiment, a hybrid input / output algorithm or an error restoration algorithm can be used), combined with the known Fourier amplitude information... The Fourier phase of the object is obtained by iterative calculation of the constraints of the object in real space (such as nonnegativity, finite support domain, etc.).

[0047] Finally, the recovered Fourier phase is combined with the known Fourier amplitude, and an inverse Fourier transform is performed to reconstruct the spatial distribution image of the object to be imaged.

[0048] The final reconstructed object image in this embodiment is as follows: Figure 5 As shown in (d), compared to the original hidden object ( Figure 5 (e) In comparison, its size and geometric features were accurately recovered, verifying the effectiveness of this method in achieving non-invasive, large field-of-view, single-exposure imaging in strongly dynamic scattering media.

[0049] This embodiment utilizes a highly coherent light source to form the object light field. After acquiring a single speckle intensity image in the far-field region, the far-field mutual coherence function is obtained based on the fourth-order moment correlation function, and the object's autocorrelation function is derived. Finally, the object image is reconstructed using a phase retrieval algorithm. Compared to existing technologies, such as speckle autocorrelation imaging (SACI) which is limited by the optical memory effect (OME) region resulting in a small imaging field of view, and moving object speckle correlation imaging (SCIMO) which requires multiple object movements and acquisitions of multiple speckle images and is unsuitable for dynamic scattering media, this invention avoids the field-of-view limitation caused by reliance on the optical memory effect, achieving a large field of view imaging far exceeding the OME range. It also avoids the experimental infeasibility caused by the need to acquire multiple speckle images in dynamic scattering environments, as imaging can be completed with a single exposure, significantly simplifying the data acquisition process and improving adaptability to dynamic scattering media and imaging efficiency.

[0050] As a further improvement to the present invention, please refer to Figure 2 Step S104 includes: Step S1041: Obtain the modulus of the far-field coherence function when the coordinates of the first preset observation point and the coordinates of the second preset observation point are equal.

[0051] The squared modulus of the far-field coherence function is obtained. Then, we set the coordinates of the two observation points to be equal, that is... This is done to obtain the diagonal value of the far-field coherence function. This physical quantity can be obtained by taking the diagonal elements of the fourth-order moment correlation function result calculated in step S103. Figure 5 The visualization result shown in (b) is essentially the same as... The distribution of, and its diagonal contains Information.

[0052] Step S1042: Eliminate the Gaussian envelope caused by the coherence length of the scattering medium in the modulus value to obtain the processed function.

[0053] After the light beam passes through the scattering medium, it is scattered into a random field. At this point, the mutual coherence function (MCF) immediately behind the scattering medium (near-field region) can be expressed as: (1) in Indicates the incident light field. and Points and position vector, Let be the coherence length of the light field. In the far-field region of the scattering medium, the mutual coherence function (MCF) of the field at two points P1 and P2, located relatively far from the back surface of the scattering medium, is given by the integral: (2) in, For wave number. and Points and The position vector. and For along and The unit vector of direction; and These are the transverse components of these unit vectors. For fully developed speckle statistics, the speckle contrast is 1, therefore the speckle field statistics follow a zero-mean circular Gaussian distribution. To obtain the mutual coherence function (MCF) of the far field, we cite the well-known relation between the second and fourth moments in the circular Gaussian field. The correlation connecting the second and fourth moment with the amplitude of the MCF is: (3) It is the spectral intensity of the scattered field. It is the mutual coherence function at the far field of scattering. When hour, Autocorrelation of speckle at the far field Based on the simultaneous equations (1), (2), and (3), the mutual coherence function of the back surface of the scattering medium is... The squared modulus of the Fourier transform of the autocorrelation function of the incident object There exists a coherence length determined by the scattering medium. The Gaussian envelope factor is introduced. The relationship between the two is expressed by the following equation: In order to obtain purity The mutual coherence function of the back surface of the scattering medium is required. This is obtained through mathematical operations embodied in the following formula: (4) in, This represents the Fourier transform. For wave number, and Points and The position vector.

[0054] Eliminating the Gaussian envelope through computation The influence of this is used to obtain the processed function, i.e. .

[0055] Step S1043: Perform an inverse Fourier transform on the processed function to obtain the autocorrelation function of the object to be imaged.

[0056] In step S1042, we obtained the processed function. According to the autocorrelation theorem, the Fourier transform of the autocorrelation function of a function is exactly equal to the squared magnitude of the Fourier transform of that function. Therefore, for By performing an inverse Fourier transform, the autocorrelation function of the object to be imaged can be directly obtained. In this embodiment, the object autocorrelation function ultimately derived through this process is as follows: Figure 5 As shown in (f), it provides crucial input data for subsequent phase retrieval algorithms to reconstruct object images.

[0057] As a further improvement of the present invention, the processed function is obtained by the following formula: in, The magnitude of the far-field coherence function is given by the coordinates of the observation point. and The value obtained when the values ​​are equal. It is a direct physical quantity obtained from a single speckle intensity image through fourth-order moment calculation and diagonal taking. The coherence length of the preset dynamic scattering medium is a physical parameter characterizing the scattering capability of the scattering medium, which depends on the type and thickness of the medium.

[0058] The spatial coordinate difference of the near-field plane corresponds to the position vector difference between two points on the back surface (near field) of the scattering medium.

[0059] It is any spatial coordinate in the far-field plane.

[0060] It is the squared magnitude of the Fourier transform of the autocorrelation function of the object to be imaged, i.e., the processed function. It eliminates the influence of the scattering medium and directly reflects the frequency domain energy distribution information of the object itself.

[0061] In actual numerical calculations, the coherence length of the scattering medium is first determined based on the known or pre-measured values. Calculate the Gaussian function.

[0062] Then, the information obtained in step S1041 The numerical value (a two-dimensional array) and the calculated value Perform element-wise division on the numerical values ​​(another two-dimensional array).

[0063] The purpose of this division operation is to eliminate the Gaussian envelope caused by the coherence length of the scattering medium in the squared modulus. Through this operation, we finally obtain a pure | that is only related to the object itself. This laid an accurate foundation for obtaining the autocorrelation function of an object through inverse Fourier transform.

[0064] As a further improvement to the present invention, please refer to Figure 3 Step S105 includes: Step S1051: Perform a Fourier transform on the autocorrelation function to obtain the Fourier amplitude information of the object to be imaged.

[0065] According to the autocorrelation theorem, the Fourier transform of the autocorrelation function is equal to the square of the magnitude of the Fourier transform of that function. Therefore, for the autocorrelation function... Performing a Fourier transform yields the following: That is, the power of the Fourier spectrum of the object.

[0066] Subsequently, by performing a square root operation on the result, the Fourier amplitude information of the object to be imaged can be obtained. This amplitude information contains the energy distribution of the object in the frequency domain and is one of the two essential elements for reconstructing an image (the other being phase).

[0067] Step S1052: Based on the Fourier amplitude information and combined with the preset real space constraints, the Fourier phase information of the object to be imaged is solved by the iterative phase recovery algorithm.

[0068] Because the autocorrelation function loses Fourier phase information, a direct inverse transform cannot obtain an image of the object. The core of this step is to use an iterative phase recovery algorithm to recover the lost phase. First, a randomly generated phase spectrum, or an initial guess about the real space (e.g., a constant matrix or a predefined shape), is compared with the known Fourier amplitude obtained in step S1051. The components are combined to form an initial complex wavefunction in the frequency domain. Iterative phase retrieval algorithms (such as hybrid input / output algorithms or error restoration algorithms) are then used for iterative calculations. The basic principle is: Fourier domain constraint: replacing the amplitude of the current iteration's frequency domain data with known Fourier amplitude information. The algorithm performs an inverse Fourier transform on the updated frequency domain data, returning it to the real space to obtain a candidate image. Then, it applies preset real space constraints to this candidate image. These constraints typically include: nonnegativity: the light intensity or intensity distribution of the object cannot be negative, therefore all negative pixels are set to zero or a small positive number; finite support region: the object occupies a finite region in real space. Pixels outside the support region can be forced to zero; real value: for intensity objects, their distribution is real value. The image with the applied real space constraints is then subjected to another Fourier transform, returning to the frequency domain, retaining its new phase, and the above process is repeated. This process is iterated until the reconstructed image meets preset convergence conditions, such as the difference between the real space images of two adjacent iterations being less than a certain threshold or reaching the maximum number of iterations. Finally, the stable phase output by the algorithm is the Fourier phase information of the object to be imaged. .

[0069] Step S1053: Combine the Fourier amplitude information and the Fourier phase information, perform inverse Fourier transform on the combined Fourier amplitude information and the Fourier phase information, and reconstruct the spatial distribution image of the object to be imaged.

[0070] Successfully recovered the Fourier phase Then, it is compared with the known Fourier amplitude information obtained in step S1051. By combining these elements, a complete complex frequency domain expression can be formed: Finally, by performing an inverse Fourier transform on the complex expression, the spatial distribution image of the object to be imaged can be directly reconstructed. The final reconstructed object image in this embodiment is compared with the original hidden object. Figure 5 As shown in (d) and (e), the effectiveness of the phase recovery procedure is verified.

[0071] As a further improvement of the present invention, the iterative phase recovery algorithm is a hybrid input-output algorithm or an error restoration algorithm.

[0072] The Hybrid Input / Output (HIO) algorithm uses a unique feedback mechanism to avoid the solution getting trapped in local minima, thus increasing the likelihood of finding the global optimum or a near-optimal solution. Its iterative process specifically includes: in the k-th iteration, the current frequency domain complex wavefunction... (Its amplitude has been replaced with the known Fourier amplitude) Perform an inverse Fourier transform to obtain the current output image in real space. .

[0073] Based on the preset real space constraints (such as nonnegativity and finite support domain), for Process the image to generate a constrained image. Specifically, within the support domain S, the pixel value remains unchanged; outside the support domain, the pixel value is forcibly set to zero.

[0074] The core update step of the HIO algorithm is to calculate the real-space input image for the next iteration. Its update formula is as follows: in, This is the real-space input image for this iteration. It is an empirical parameter between 0.5 and 1 (typically between 0.7 and 0.9). This formula introduces a negative feedback term outside the constrained region, which helps the algorithm escape local minima.

[0075] The updated Perform a Fourier transform to obtain new frequency domain data. .

[0076] reserve The phase is then replaced with the known Fourier amplitude. This forms the input for the next iteration. Repeat the above steps until convergence.

[0077] The Error Recovery (ER) algorithm is a more direct and simpler phase retrieval method that gradually reduces the error by imposing strict constraints in real space. Its iterative process is as follows: In the k-th iteration, the current frequency domain complex wave function (Its amplitude has been replaced with the known Fourier amplitude) Perform an inverse Fourier transform to obtain the image in real space. .

[0078] The core update step of the ER algorithm: By strictly applying all preset real-space constraints, the real-space input image for the next iteration is directly obtained. .

[0079] That is, only regions within the supporting domain that satisfy the non-negativity condition are retained. The value of is forced to zero in all other areas.

[0080] Will Perform a Fourier transform to obtain new frequency domain data. .

[0081] reserve The phase is determined and its amplitude is replaced with the known Fourier amplitude. This forms the input for the next iteration. Repeat the above steps until convergence.

[0082] In the imaging application through dynamically scattering media of this invention, the HIO algorithm, due to its stronger global search capability, typically yields higher-quality reconstruction results; while the ER algorithm has a simpler structure and faster convergence speed. In practice, the two algorithms are often used in combination, for example, performing multiple HIO iterations followed by one ER iteration, to balance convergence speed and solution quality, thereby efficiently and accurately reconstructing the image of the object to be imaged.

[0083] As a further improvement of the present invention, the preset real space constraint condition includes at least one of the following.

[0084] The spatial distribution of the object to be imaged is nonnegative; this is the most fundamental and powerful constraint. In most optical imaging scenarios, the object to be imaged represents light intensity or intensity distribution. By physical definition, intensity is a nonnegative physical quantity, meaning its value is greater than or equal to zero at any spatial location. Therefore, during the iteration process, whenever the algorithm generates a candidate image in real space, we apply a nonnegativity constraint, forcing all pixels in the image with negative values ​​to be set to zero or a preset, extremely small nonnegative value (such as 0). This constraint effectively suppresses non-physical oscillations and artifacts generated during reconstruction, greatly reduces the solution space, and guides the algorithm to converge to the correct solution.

[0085] The spatial distribution of the object to be imaged has a finite support domain; this constraint means that the area occupied by the object in real space is finite, not infinitely expanding. That is, the object exists within a defined, bounded region called the support domain; outside this region, the object's intensity value is zero. This prior knowledge can come from a general understanding of the experimental scenario or be automatically estimated by the algorithm. During the iteration process, the finite support domain constraint is applied to the candidate image: within the preset support domain, pixel values ​​evolve freely according to the algorithm; outside the support domain, all pixel values ​​are forced to zero. This constraint provides additional spatial information, effectively solves the ambiguity problem in phase retrieval, and significantly accelerates the convergence speed of the algorithm.

[0086] The spatial distribution of the object to be imaged is real-valued. This constraint applies when the object is an intensity object whose spatial distribution is described by a real-valued function (as opposed to a complex-valued object function that includes both amplitude and phase information). The real-valued constraint means that the Fourier transform of the object satisfies Hermitian symmetry, i.e. During the iteration process, this constraint can be enforced by applying this symmetry in the frequency domain. This provides the algorithm with additional redundant information, which helps to recover the Fourier phase more stably and accurately.

[0087] In the transmission scattering medium imaging method of this embodiment, the combined application of one or more of the above-mentioned real-space constraints can provide crucial supplementary information for the phase retrieval algorithm, compensating for the information loss caused by phase loss. This enables the algorithm to stably and accurately reconstruct a clear image of the object to be imaged from the autocorrelation function derived from a single speckle image, such as... Figure 5 As shown in (d). These constraints are an important part of ensuring the successful implementation of the technical solution of the present invention in practice.

[0088] As a further improvement to the present invention, please see [link / reference]. Figure 4 Step S1052 includes: S1052a: Combine the current Fourier amplitude to be iterated with the preset guessed phase and then perform an inverse Fourier transform to obtain the candidate image in real space. Generate an initial guessed phase distribution. This phase can be randomly generated or set based on prior knowledge. The known Fourier amplitude information... (Obtained from the square root of the Fourier transform of the autocorrelation function) and this initial guess phase Combining these elements, we form the initial complex wavefunction in the frequency domain: .

[0089] S1052b: Apply the real space constraint condition to the candidate image to obtain the updated image; The frequency domain complex wave function of the current iteration (In the first iteration) Perform an inverse Fourier transform to obtain candidate images in real space. .Right now: S1052c: Perform a Fourier transform on the updated image to obtain the updated frequency domain data; The candidate images obtained in step S1052b Apply the preset real-space constraints (such as nonnegativity, finite support region, etc.) to obtain the updated result. .

[0090] For example, if the constraint is that the support domain S is non-negative, then: S1052d: Retain the phase in the updated frequency domain data and replace the amplitude of the updated frequency domain data with the Fourier amplitude information to form the iterated frequency domain input; The updated image obtained after applying constraints Perform a Fourier transform to obtain the updated frequency domain data. .

[0091] Subsequently, retain Phase information The amplitude of the updated frequency domain data is replaced with the known, fixed Fourier amplitude information. This step ensures that the amplitude constraints in the frequency domain are satisfied. This results in the iterative frequency domain input. For use in the next iteration: S1052e: Continuously iterate to form new frequency domain inputs until the preset convergence condition is met or the maximum number of iterations is reached, and use the phase in the finally updated frequency domain data as the Fourier phase of the object to be imaged.

[0092] The result obtained in step S1052d As new input, steps S1052b to S1052d are iteratively repeated. This loop continues until a preset convergence condition is met (e.g., the real-space image obtained from two consecutive iterations). (The difference between them is less than a certain threshold) or the maximum number of iterations is reached.

[0093] When the iteration terminates, the phase in the finally updated frequency domain data is taken as the Fourier phase of the object to be imaged. .

[0094] This embodiment clearly outlines the core iterative loop of phase retrieval. This process repeatedly switches between the frequency domain (applying amplitude constraints) and real space (applying physical constraints), using constraint information to gradually approximate and ultimately lock the correct Fourier phase. This process is the computational core of the imaging method of this invention, enabling it to successfully reconstruct an object image from a single speckle image. Its effectiveness and feasibility have been proven through... Figure 5 The successful reconstruction results shown in (d) are verified.

[0095] The present invention also provides a non-invasive imaging device that transmits through a dynamic scattering medium; please refer to [link to relevant documentation]. Figure 6 ,include: A light source is used to emit a highly coherent light beam. In this embodiment, the light source is a laser. The laser light generated by the laser has high spatial and temporal coherence, which is a prerequisite for ensuring that the scattered light field satisfies specific statistical properties, thus enabling the application of the FTMCF method.

[0096] A spatial light modulator (SLM) is positioned along the propagation path of the incident beam emitted from a light source to modulate the wavefront of the beam, thereby forming an object light field. Specifically, by loading a specially designed computer-generated hologram (e.g., using a checkerboard pattern) onto the SLM, the complex amplitude (including amplitude and phase) of the beam can be precisely controlled, thus generating an object light field with a specific intensity distribution pattern (such as the letters "S" or "N"). This object light field is equivalent to the object to be imaged.

[0097] A scattering medium is placed along the propagation path of the object's light field modulated by a spatial light modulator. Its function is to scatter the object's light field to form a speckle field. In this embodiment, the scattering medium is a dynamic scattering medium, such as a piece of fresh chicken breast (approximately 1-2 mm thick). Because its internal structure is constantly changing, it simulates the dynamic scattering environment commonly found in practical applications such as biological tissues. After the object's light field passes through this medium, the information is encoded in a randomly distributed speckle pattern.

[0098] An image sensor, disposed in the far-field region of the scattering medium, is used to acquire a single speckle intensity image. In this embodiment, the image sensor is a charge-coupled device (CCD) camera. It is placed at a sufficiently far distance (e.g., 300 mm) from the back surface of the scattering medium to satisfy the far-field condition. This CCD camera is capable of capturing the intensity distribution of a single frame of the speckle field in a single operation. Its effective pixel array size is 1392×1040, and the pixel size is 6.45μm×6.45μm. The acquired single-frame speckle image is shown below. Figure 5 As shown in (a).

[0099] A data processing module, electrically connected to an image sensor (CCD), receives and processes the speckle intensity image to reconstruct an object image. This module can be a computer or embedded system running a dedicated algorithm program. The processing flow includes: calculating the far-field cross-coherence function (MCF): based on a single speckle intensity image, utilizing circular Gaussian statistical properties, the squared magnitude of the far-field MCF is obtained by calculating its fourth-order moment correlation function. Deriving the object autocorrelation function: based on the theoretical relationship between the MCF and the object light field autocorrelation function, the autocorrelation function of the object to be imaged is derived from the MCF. Reconstructing the object image using a phase retrieval algorithm: performing a Fourier transform on the obtained object autocorrelation function to obtain the Fourier amplitude, then combining real space constraints, recovering the Fourier phase using an iterative phase retrieval algorithm (such as HIO or ER algorithm), and finally reconstructing the spatial distribution image of the object using an inverse Fourier transform, such as... Figure 5 As shown in (d).

[0100] Working principle: Coherent light emitted by the laser is modulated by a spatial light modulator into a light field for the target object. This light field passes through a dynamic scattering medium, forming random speckle patterns. A CCD camera located in the far field captures a speckle intensity image in a single exposure. Subsequently, the data processing module executes the core algorithm of the FTMCF method to extract the object's autocorrelation information from the single speckle image and finally reconstruct a clear image of the object. The entire device achieves non-invasive, large field-of-view, single-exposure imaging of objects behind a dynamic scattering medium.

[0101] As a further improvement of the present invention, a 4f optical component is disposed between the spatial light modulator and the scattering medium. This 4f optical component is mainly used to perform the following processing on the modulated beam emitted from the spatial light modulator: Filtering: When modulating the beam, the pixel structure of the spatial light modulator (such as an SLM) may introduce unwanted diffraction orders and high-frequency noise. The first lens (L3) of the 4f system performs a Fourier transform on the light field of the SLM surface, presenting its spectrum on the confocal plane. The pinhole placed there acts as a spatial filter, allowing only the spectral components of the desired diffraction order (usually zeroth or positive first order) to pass through, while effectively blocking other stray light and noise. Collimation and Reconstruction: The second lens (L4) performs an inverse Fourier transform on the clean spectrum filtered through the pinhole, reconstructing a clean, high-quality object light field on the output surface of the system (i.e., the plane where the scattering medium is located). Beam Expanding / Contracting: By selecting lens combinations with different focal lengths, the 4f system can also simultaneously enlarge or reduce the beam size to adapt to the size requirements of the subsequent scattering medium and detector. Introducing this 4f optical component significantly improves the quality of the generated object light field. It removes noise introduced by the SLM through filtering and ensures, through accurate Fourier transform and inverse transform, that the target pattern loaded on the SLM is precisely transmitted to the incident surface of the scattering medium. A clean and accurate initial object light field is crucial for the subsequent successful image reconstruction from speckle using the FTMCF method. This component effectively improves the signal-to-noise ratio and imaging reliability of the entire device.

[0102] As a further improvement of the present invention, the data processing module is configured to perform the following operations: The module performs correlation calculations on the speckle intensity image. It receives a single speckle intensity image acquired by an image sensor (such as a CCD). First, it calculates the fourth-order moment correlation function of the speckle image, specifically by calculating the spatial autocorrelation of speckle intensity fluctuations. According to circular Gaussian statistical theory, the result of this calculation directly corresponds to the squared modulus of the far-field mutual coherence function (MCF). . Figure 5 (b) The result of this calculation is visualized.

[0103] The relevant results are then subjected to local scaling and Fourier transform. Local scaling: The module first obtains the data on the diagonal of the aforementioned relevant results, i.e. Subsequently, a crucial mathematical process is performed to eliminate the coherence length of the scattering medium. The Gaussian envelope effect is introduced. This treatment is described by equation (4), which is essentially a local scaling operation designed to reduce the local size of the envelope. Extract the pure functions that are only related to the object. Fourier transform: Next, the module applies the scaled function... Perform an inverse Fourier transform. According to the autocorrelation theorem, the result of this transform is the autocorrelation function of the object to be imaged. ,like Figure 5 As shown in (f).

[0104] The phase retrieval algorithm is applied to output an image of the object. This operation is used to finally complete image reconstruction. The module first processes the obtained object autocorrelation function. Perform a Fourier transform and take the square root to obtain the Fourier amplitude information of the object. Subsequently, the module applies a phase retrieval algorithm (such as a hybrid input / output algorithm or an error restoration algorithm). This algorithm utilizes the known Fourier amplitude and combines it with the constraints of the object in real space (such as nonnegativity and a finite support domain) to iteratively recover the Fourier phase information of the object. Finally, the recovered phase is combined with the known amplitude to perform an inverse Fourier transform, outputting the final reconstructed image of the object, such as... Figure 5 As shown in (d).

[0105] This embodiment clarifies the programmed operation flow of the data processing module. These three steps are interconnected and completely transform a single speckle intensity image into a clear object image. This is the software core for the device of this invention to realize its function.

[0106] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The above embodiments merely illustrate preferred implementations of the present invention, and their descriptions are quite specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A non-invasive imaging method through a dynamic scattering medium, characterized in that, include: The preset coherent object beam illuminates the object to be imaged, forming an object beam field; In the far field region, a speckle intensity image of the speckle field formed by the object light field passing through a preset dynamic scattering medium is acquired, wherein the far field region is the region of the preset dynamic scattering medium through which the object light field passes. Based on the fourth-order moment correlation function of the speckle intensity image, the far-field mutual coherence function is obtained through analysis. Based on the functional relationship between the far-field mutual coherence function and the object field autocorrelation function, the autocorrelation function of the object to be imaged is obtained. Based on the phase retrieval algorithm, the image of the object to be imaged is reconstructed using the autocorrelation function.

2. The non-invasive imaging method through a dynamic scattering medium according to claim 1, characterized in that, The step of analyzing and obtaining the autocorrelation function of the object to be imaged based on the functional relationship between the far-field cross-coherence function and the object field autocorrelation function includes: Obtain the modulus of the far-field coherence function when the coordinates of the first preset observation point and the coordinates of the second preset observation point are equal; The Gaussian envelope caused by the coherence length of the scattering medium in the modulus is eliminated to obtain the processed function; The processed function is subjected to an inverse Fourier transform to obtain the autocorrelation function of the object to be imaged.

3. The non-invasive imaging method through a dynamic scattering medium according to claim 2, characterized in that, The processed function is obtained through the following formula: ； in, Let be the squared magnitude of the far-field coherence function; The coherence length of the preset dynamic scattering medium; The spatial coordinate difference in the near-field plane; The value is the squared magnitude of the Fourier transform of the autocorrelation function of the object to be imaged.

4. The non-invasive imaging method through a dynamic scattering medium according to claim 1, characterized in that, The method of reconstructing the image of the object to be imaged using the phase retrieval algorithm and the autocorrelation function includes: The autocorrelation function is subjected to Fourier transform to obtain the Fourier amplitude information of the object to be imaged; Based on the Fourier amplitude information and combined with the preset real space constraints, the Fourier phase information of the object to be imaged is solved by the iterative phase recovery algorithm. The Fourier amplitude information and the Fourier phase information are combined, and the combined Fourier amplitude information and the Fourier phase information are subjected to inverse Fourier transform to reconstruct the spatial distribution image of the object to be imaged.

5. The non-invasive imaging method through a dynamic scattering medium according to claim 4, characterized in that, The iterative phase recovery algorithm is either a hybrid input / output algorithm or an error recovery algorithm.

6. The non-invasive imaging method through a dynamic scattering medium according to claim 4, characterized in that, The preset real space constraints include at least one of the following: The spatial distribution of the object to be imaged is non-negative; The spatial distribution of the object to be imaged has a finite support domain; The spatial distribution of the object to be imaged has real value properties.

7. The non-invasive imaging method through a dynamic scattering medium according to claim 4, characterized in that, The step of solving the Fourier phase of the object to be imaged using an iterative phase retrieval algorithm based on the Fourier amplitude information and in combination with preset real-space constraints includes: The current Fourier amplitude to be iterated is combined with the preset guessed phase and then inverse Fourier transform is performed to obtain the candidate image in real space. The real space constraint is applied to the candidate image to obtain the updated image; Perform a Fourier transform on the updated image to obtain the updated frequency domain data; The phase in the updated frequency domain data is retained, and the amplitude of the updated frequency domain data is replaced with the Fourier amplitude information to form the iterated frequency domain input; The frequency domain input is continuously iterated to form new inputs until the preset convergence condition is met or the maximum number of iterations is reached. The phase in the finally updated frequency domain data is used as the Fourier phase of the object to be imaged.

8. A non-invasive imaging device that transmits through a dynamic scattering medium, characterized in that, include: A light source used to emit a beam of light; A spatial light modulator is used to modulate the light beam to form an object light field; A scattering medium is placed in the propagation path of the object's light field to scatter the object's light field and form a speckle field. An image sensor, disposed in the far-field region of the scattering medium, is used to acquire a single speckle intensity image; The data processing module is used to process the speckle intensity image to reconstruct an object image, wherein the processing includes calculating the far-field mutual coherence function, deriving the object autocorrelation function, and reconstructing the object image using a phase retrieval algorithm.

9. The non-invasive imaging device for transmitting through a dynamic scattering medium as described in claim 8, characterized in that, A 4f optical component, including a lens group and a pinhole, is disposed between the spatial light modulator and the scattering medium for expanding, collimating, and filtering the light beam.

10. The non-invasive imaging device for transmitting through a dynamic scattering medium as described in claim 8, characterized in that, The data processing module is configured to perform the following operations: Perform correlation calculations on the speckle intensity image; The relevant results are locally scaled and Fourier transformed; the phase retrieval algorithm is applied to output the object image.

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