Scattering imaging method, device, computer equipment and storage medium for white light illumination

Abstract

The application relates to a white light illumination scattering imaging method, device, computer equipment and storage medium. The method comprises the following steps: obtaining a target speckle pattern formed after a target object is imaged through a scattering medium under white light illumination; performing iterative processing on the target speckle pattern through a preset iterative phase recovery algorithm to obtain a target phase distribution corresponding to an intensity point spread function in a frequency domain; and performing deconvolution processing on the target speckle pattern and the target phase distribution to complete reconstruction of the target object. The method can improve the application scene of light scattering imaging technology.

Description

Technical Field

[0001] This application relates to the field of scattering imaging technology, and in particular to a scattering imaging method, apparatus, computer device and storage medium under white light illumination. Background Technology

[0002] Optical imaging is a crucial means for humans to acquire information, playing a vital role in numerous fields such as daily life, national defense, and biomedicine. However, several factors limit its further application, one of which is "scattering": due to the random perturbation of incident light by the scattering medium, the light carrying information about the object cannot be focused and formed on the imaging surface, instead exhibiting a speckle pattern resembling random noise. Therefore, how to achieve object imaging through a scattering medium is a problem worthy of attention.

[0003] Currently, object imaging through scattering media can also be understood as scattering imaging, and the key technologies involved are mainly deconvolution and speckle autocorrelation. Deconvolution can achieve scattering imaging under broad-spectrum illumination; however, the need to pre-determine the intensity point spread function of the imaging system at its input significantly limits its application. Meanwhile, speckle autocorrelation, relying on the wavelength-sensitive Wiener-Khinchin theorem and its subsequent steps, is only applicable to scattering imaging under quasi-monochromatic illumination, thus limiting its application. Therefore, existing scattering imaging methods suffer from limited application scenarios. Summary of the Invention

[0004] Therefore, it is necessary to provide a white light illumination scattering imaging method, apparatus, computer equipment, and storage medium that can improve the application scenarios of the above-mentioned technical problems.

[0005] A white light illumination scattering imaging method, the method comprising:

[0006] Acquire the speckle pattern of the target object after imaging through a scattering medium under white light illumination;

[0007] The target speckle pattern is iteratively processed by a preset iterative phase recovery algorithm to obtain the target phase distribution corresponding to the intensity point spread function in the frequency domain;

[0008] The target object is reconstructed by performing deconvolution processing based on the target speckle map and the target phase distribution.

[0009] In one embodiment, the step of iteratively processing the target speckle map using a preset iterative phase recovery algorithm to obtain the target phase distribution of the intensity point spread function in the frequency domain includes:

[0010] Obtain the current phase distribution of the intensity point spread function in the frequency domain;

[0011] Based on the target speckle pattern and the current phase distribution, the spatial complex amplitude distribution corresponding to the target object can be recovered;

[0012] The spatial complex amplitude distribution is spatially constrained to obtain the updated spatial complex amplitude distribution;

[0013] Based on the updated spatial complex amplitude distribution and the target speckle map, the current phase distribution is updated, and the process returns to the step of restoring the spatial complex amplitude distribution corresponding to the target object based on the target speckle map and the current phase distribution, continuing until the iteration stop condition is met, and the updated current phase distribution is determined as the target phase distribution corresponding to the intensity point spread function in the frequency domain.

[0014] In one embodiment, recovering the spatial complex amplitude distribution corresponding to the target object based on the target speckle pattern and the current phase distribution includes:

[0015] The corresponding speckle amplitude distribution and speckle phase distribution are obtained based on the target speckle pattern.

[0016] Based on the speckle phase distribution and the current phase distribution, the first phase distribution of the target object in the frequency domain is obtained;

[0017] Based on the speckle amplitude distribution and the first phase distribution, the first spectral distribution corresponding to the target object is recovered;

[0018] Performing an inverse Fourier transform on the first spectral distribution yields the spatial complex amplitude distribution corresponding to the target object.

[0019] In one embodiment, updating the current phase distribution based on the updated spatial complex amplitude distribution and the target speckle map includes:

[0020] Perform an inverse Fourier transform on the updated spatial complex amplitude distribution to obtain the second spectral distribution corresponding to the target object;

[0021] The second phase distribution of the target object in the frequency domain is obtained based on the second spectral distribution.

[0022] The current phase distribution is updated based on the speckle phase distribution and the second phase distribution.

[0023] In one embodiment, the step of spatially constraining the spatial complex amplitude distribution to obtain the updated spatial complex amplitude distribution includes:

[0024] The constrained support is obtained based on the spatial complex amplitude distribution;

[0025] Based on the aforementioned constraint support, the spatial complex amplitude distribution is subjected to spatial constraints to obtain an updated spatial complex amplitude distribution.

[0026] In one embodiment, obtaining the constrained support based on the spatial complex amplitude distribution includes:

[0027] The spatial amplitude distribution corresponding to the target object is obtained based on the spatial complex amplitude distribution.

[0028] Constraint support is obtained by selecting a preset number of pixel positions from the spatial amplitude distribution based on the pixel value of each pixel.

[0029] In one embodiment, the step of deconvolution processing based on the target speckle map and the target phase distribution to complete the reconstruction of the target object includes:

[0030] Based on the speckle spectrum distribution in the frequency domain corresponding to the target speckle pattern and the target phase distribution, the target spectrum distribution in the frequency domain corresponding to the target object is recovered.

[0031] A Fourier transform is performed on the target spectral distribution to reconstruct the object image corresponding to the target object.

[0032] A white light-illuminated scattering imaging device, the device comprising:

[0033] The acquisition module is used to acquire the target speckle pattern formed after the target object is imaged through a scattering medium under white light illumination;

[0034] The iterative recovery module is used to iteratively process the target speckle map using a preset iterative phase recovery algorithm to obtain the target phase distribution corresponding to the intensity point spread function in the frequency domain;

[0035] The reconstruction module is used to perform deconvolution processing based on the target speckle map and the target phase distribution to complete the reconstruction of the target object.

[0036] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0037] Acquire the speckle pattern of the target object after imaging through a scattering medium under white light illumination;

[0038] The target speckle pattern is iteratively processed by a preset iterative phase recovery algorithm to obtain the target phase distribution corresponding to the intensity point spread function in the frequency domain;

[0039] The target object is reconstructed by performing deconvolution processing based on the target speckle map and the target phase distribution.

[0040] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0041] Acquire the speckle pattern of the target object after imaging through a scattering medium under white light illumination;

[0042] The target speckle pattern is iteratively processed by a preset iterative phase recovery algorithm to obtain the target phase distribution corresponding to the intensity point spread function in the frequency domain;

[0043] The target object is reconstructed by performing deconvolution processing based on the target speckle map and the target phase distribution.

[0044] The aforementioned white light illumination scattering imaging method, apparatus, computer equipment, and storage medium acquire a single target speckle pattern formed and collected after the target object is imaged through a scattering medium under white light illumination. A preset iterative phase retrieval algorithm is used to iteratively process this target speckle pattern to accurately recover the target phase distribution corresponding to the intensity point spread function of the imaging system in the frequency domain. This target phase distribution is key information of the intensity point spread function. Therefore, based on the target speckle pattern and the target phase distribution recovered from the intensity point spread function, deconvolution processing can accurately reconstruct the target object hidden behind the scattering medium. Thus, without prior intrusion into the imaging system, scattering imaging under white light illumination (broadband illumination) can be accurately achieved based on a single target speckle pattern formed and collected after the target object is imaged through a scattering medium in a single exposure. This accurately realizes single-exposure, non-invasive scattering imaging under broadband illumination, thereby expanding the application scenarios of this scattering imaging method. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating a white light illumination scattering imaging method in one embodiment;

[0046] Figure 2 This is a schematic diagram illustrating the principle of iteratively processing the target speckle map based on a preset iterative phase recovery algorithm in one embodiment to obtain the target phase distribution corresponding to the intensity point spread function in the frequency domain.

[0047] Figure 3 This is a schematic diagram illustrating the effect of reconstructing the target object based on a single target speckle pattern formed after imaging the target object through a scattering medium under white light illumination, and the phase distribution of the system's intensity point spread function, in one embodiment.

[0048] Figure 4This is a schematic diagram of the architecture of a light scattering imaging system in one embodiment;

[0049] Figure 5 This is a schematic diagram illustrating the effect of reconstructing a target object using a white light illumination-based scattering imaging method in one embodiment.

[0050] Figure 6 This is a structural block diagram of a white light illumination scattering imaging device in one embodiment;

[0051] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] In one embodiment, such as Figure 1 As shown, a white light illumination scattering imaging method is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0054] Step 102: Obtain the target speckle pattern formed by imaging the target object through the scattering medium under white light illumination.

[0055] The target speckle pattern is the speckle pattern formed on the image plane of a target object after it has been imaged through a scattering medium in a light scattering imaging system using white light (broad spectrum) as the light source. The scattering medium includes, but is not limited to, frosted glass, water, and biological tissue.

[0056] Specifically, in a light scattering imaging system, light emitted from a white light source sequentially passes through the target object on the object plane and is modulated by a scattering medium placed between the object plane and the image plane, forming a target speckle pattern corresponding to the target object on the image plane. An image acquisition device positioned on the image plane acquires this target speckle pattern and sends it to a terminal. The terminal then reconstructs an image of the target object based on this speckle pattern, using the light scattering imaging method provided in this application. The image acquisition device can be a camera, such as an SCMOS or CMOS camera.

[0057] In one embodiment, the white light source in the light scattering imaging system involved in this application may also be replaced with other light sources capable of emitting broad-spectrum light, without specific limitations.

[0058] Step 104: The target speckle pattern is iteratively processed using a preset iterative phase recovery algorithm to obtain the target phase distribution corresponding to the intensity point spread function in the frequency domain.

[0059] Among them, the target phase distribution corresponding to the intensity point spread function in the frequency domain is the key information of the intensity point spread function, which can also be understood as the phase distribution corresponding to the system optical transfer function of the light scattering imaging system.

[0060] Specifically, the terminal performs iterative phase recovery processing on the target speckle map corresponding to the target object based on a preset iterative phase recovery algorithm until the iteration stops when the iteration stopping condition is met, thus obtaining the target phase distribution in the frequency domain corresponding to the intensity point spread function of the light scattering imaging system. It can be understood that iterative processing of the target speckle map based on the iterative phase recovery algorithm can more accurately estimate (recover) the target phase distribution in the frequency domain corresponding to the intensity point spread function.

[0061] Since phase distribution is more important than amplitude distribution in the spectral distribution, recovering the target phase distribution corresponding to the intensity point spread function in the frequency domain is equivalent to recovering the key information of the intensity point spread function, or approximately recovering the intensity point spread function. Based on the approximately recovered intensity point spread function and the target speckle map, the target object can be accurately reconstructed through deconvolution.

[0062] Step 106: Perform deconvolution processing based on the target speckle map and the target phase distribution to complete the reconstruction of the target object.

[0063] Specifically, after the terminal recovers the target phase distribution corresponding to the intensity point spread function in the frequency domain based on the target speckle map, it performs deconvolution processing on the target speckle map and the recovered target phase distribution to reconstruct the target object to be imaged, that is, to reconstruct / recover the object image corresponding to the target object.

[0064] In one embodiment, when the target object to be imaged (observed) is sufficiently small (i.e., within the range of the system's optical memory effect), the entire light scattering imaging system can be approximated as a linear translation-invariant system. Under white light illumination, according to the theory of incoherent optical information processing, it is known that the object image corresponding to the target object, the target speckle pattern, and the intensity point spread function of the light scattering imaging system have the following mapping relationship as shown in formula (1) (the coordinate system is omitted for ease of description):

[0065] I = O * h (1)

[0066] Where O and I are the object function (object image) and the distribution function of the target speckle map, respectively, h is the intensity point spread function of the system, and the symbol "*" represents the convolution operation.

[0067] Performing a Fourier transform on the above formula (1) yields formula (2). Further performing a rearrangement and inverse Fourier transform on formula (2) yields formula (3):

[0068] FT{I}=FT{O}·FT{h} (2)

[0069]

[0070] Where FT{} denotes Fourier transform, FT -1 {} represents the inverse Fourier transform.

[0071] According to the above formula (3), if the intensity point spread function h of the light scattering imaging system is measured in advance, theoretically, based on any target speckle pattern formed after the target object is imaged by the light scattering imaging system, the object function O corresponding to the target object can be directly solved by deconvolution, that is, the target object can be reconstructed to obtain the corresponding object image.

[0072] Since the phase distribution is more important than the amplitude distribution in the spectral distribution, for the spectral distribution FT{h} corresponding to the intensity point spread function h, its amplitude distribution is ignored, while the phase distribution, which is the key information, is retained. Therefore, by processing the above formulas (2) and (3), the following formulas (4) and (5) are obtained respectively:

[0073] FT{I}≈FT{O}·Phase{FT{h}} (4)

[0074]

[0075] Here, Phase{} represents the phase operator.

[0076] According to the above formula (5), if the target phase distribution Phase{FT{h}} corresponding to the intensity point spread function h can be accurately recovered based on the target speckle map I corresponding to the target object, then according to the target speckle map I and the target phase distribution Phase{FT{h}}, the object image corresponding to the target object can be reconstructed by performing deconvolution processing according to formula (5).

[0077] The aforementioned white light illumination-based scattering imaging method acquires a single target speckle pattern formed and collected after the target object is imaged through a scattering medium under white light illumination. A preset iterative phase retrieval algorithm is used to iteratively process this speckle pattern to accurately recover the target phase distribution in the frequency domain corresponding to the intensity point spread function of the imaging system. This target phase distribution is key information for the intensity point spread function. Therefore, based on the target speckle pattern and the target phase distribution recovered from the intensity point spread function, deconvolution processing can accurately reconstruct the target object hidden behind the scattering medium. Thus, without prior intrusion into the imaging system, scattering imaging under white light illumination (broadband illumination) can be accurately achieved based on a single target speckle pattern formed and collected after the target object is imaged through a scattering medium in a single exposure. This accurately realizes single-exposure, non-invasive scattering imaging under broadband illumination, thereby expanding the application scenarios of this scattering imaging method.

[0078] In one embodiment, step 104 includes: obtaining the current phase distribution of the intensity point spread function in the frequency domain; recovering the spatial complex amplitude distribution corresponding to the target object based on the target speckle map and the current phase distribution; applying spatial constraints to the spatial complex amplitude distribution to obtain an updated spatial complex amplitude distribution; updating the current phase distribution based on the updated spatial complex amplitude distribution and the target speckle map, and returning to the step of recovering the spatial complex amplitude distribution corresponding to the target object based on the target speckle map and the current phase distribution to continue execution until the iteration stop condition is met, and determining the updated current phase distribution as the target phase distribution corresponding to the intensity point spread function in the frequency domain.

[0079] The spatial complex amplitude distribution corresponding to the target object refers to the complex amplitude distribution of the target object in the spatial domain, specifically including the spatial amplitude distribution and the spatial phase distribution. The iteration stopping condition is the condition or basis used to determine whether to stop the iterative process of the iterative phase recovery algorithm, including but not limited to the number of iterations being greater than or equal to a preset number, which is customized according to the actual situation.

[0080] Specifically, in the iterative processing of the target speckle map based on the iterative phase recovery algorithm, the terminal obtains the current phase distribution of the intensity point spread function in the current iteration. Based on the target speckle map corresponding to the target object and the obtained current phase distribution, the spatial complex amplitude distribution of the target object in the spatial domain is recovered. Dynamic spatial constraints are applied to the spatial complex amplitude distribution in the spatial domain to update the spatial complex amplitude distribution and obtain the updated spatial complex amplitude distribution. Based on the updated spatial complex amplitude distribution and the target speckle map, the current phase distribution in the current iteration is updated. If the iteration stopping condition is met, the iteration stops, and the updated current phase distribution in the current iteration is used as the target phase distribution of the intensity point spread function in the frequency domain. If the iteration stopping condition is not met, the next iteration process is started, and in the next iteration process, the updated current phase distribution in the current iteration is used as the current phase distribution in the next iteration.

[0081] In one embodiment, during the initial (first) iteration, a phase map is randomly generated as the current phase distribution corresponding to the intensity point spread function in the initial iteration. It can be understood that this randomly generated phase map is an initial guess of key information about the intensity point spread function of the light scattering imaging system.

[0082] In the above embodiments, by performing iterative phase recovery processing on the target speckle map, the target phase distribution corresponding to the intensity point spread function of the system in the frequency domain can be accurately recovered, so that the target object can be accurately reconstructed based on the recovered target phase distribution and the target speckle map.

[0083] In one embodiment, recovering the spatial complex amplitude distribution corresponding to the target object based on the target speckle pattern and the current phase distribution includes: obtaining the corresponding speckle amplitude distribution and speckle phase distribution based on the target speckle pattern; obtaining the first phase distribution of the target object in the frequency domain based on the speckle phase distribution and the current phase distribution; recovering the first spectral distribution corresponding to the target object based on the speckle amplitude distribution and the first phase distribution; and performing an inverse Fourier transform on the first spectral distribution to obtain the spatial complex amplitude distribution corresponding to the target object.

[0084] Specifically, a Fourier transform is performed on the target speckle pattern to obtain the corresponding speckle amplitude distribution and speckle phase distribution. Based on the current phase distribution of the intensity point spread function in the current iteration and the speckle phase distribution, the first phase distribution of the target object in the frequency domain is recovered. The speckle amplitude distribution is taken as the first amplitude distribution of the target object in the frequency domain. Based on the first amplitude distribution and the first phase distribution, the first spectral distribution of the target object in the frequency domain is recovered. Finally, an inverse Fourier transform is performed on the first spectral distribution to obtain the spatial complex amplitude distribution of the target object in the spatial domain.

[0085] In one embodiment, processing the above formulas (4) and (5) yields the following formulas (6) and (7). In the current iteration, the terminal recovers the first amplitude distribution and the first phase distribution of the target object in the frequency domain according to the speckle phase distribution and the current phase distribution, referring to the mapping relationship shown in formulas (6) and (7).

[0086] |FT{I}|≈|FT{O}| (6)

[0087] Phase{FT{O}}≈Phase{FT{I}}-Phase{FT{h}} (7)

[0088] Here, || represents the amplitude operator.

[0089] Based on formulas (6) and (7), it can be seen that the amplitude distributions of the object image and the target speckle pattern in the frequency domain are approximately equal, and the phase difference between the phase distribution of the target speckle pattern in the frequency domain and the phase distribution of the intensity point spread function in the frequency domain is approximately equal to the phase distribution of the object image in the frequency domain. Therefore, in the current iteration, the first spectral distribution corresponding to the target object can be recovered based on the current phase distributions of the target speckle pattern and the intensity point spread function in the current iteration.

[0090] In the above embodiments, based on the target speckle pattern and the current phase distribution corresponding to the intensity point spread function in the current iteration, the first spectral distribution corresponding to the target object is accurately recovered, so that the spatial complex amplitude distribution of the target object in the spatial domain can be accurately obtained based on the first spectral distribution.

[0091] In one embodiment, updating the current phase distribution based on the updated spatial complex amplitude distribution and the target speckle map includes: performing an inverse Fourier transform on the updated spatial complex amplitude distribution to obtain a second spectral distribution corresponding to the target object; obtaining a second phase distribution of the target object in the frequency domain based on the second spectral distribution; and updating the current phase distribution based on the speckle phase distribution and the second phase distribution.

[0092] Specifically, in the current iteration, the terminal performs an inverse Fourier transform on the updated spatial complex amplitude distribution to obtain the second spectral distribution of the target object in the frequency domain. The second spectral distribution is then phase-processed to obtain the second phase distribution of the target object in the frequency domain. Based on the speckle phase distribution and the second phase distribution, the current phase distribution of the intensity point spread function in the current iteration is updated.

[0093] In one embodiment, after the terminal recovers the second phase distribution of the target object in the frequency domain, it subtracts the speckle phase distribution from the second phase distribution and updates the current phase distribution in the current iteration according to the phase difference obtained by the subtraction, that is, it updates the current phase distribution in the current iteration to the phase difference.

[0094] In the above embodiments, the current phase distribution in the current iteration is updated based on the spatial complex amplitude distribution and target speckle map that are recovered and dynamically constrained in the current iteration, so as to obtain a more accurate current phase distribution. This allows the target object to be accurately reconstructed based on the accurately recovered current phase distribution and target speckle map when the iteration stops.

[0095] In one embodiment, obtaining an updated spatial complex amplitude distribution by spatially constraining the spatial complex amplitude distribution includes: obtaining constraint support based on the spatial complex amplitude distribution; and applying spatial constraints to the spatial complex amplitude distribution based on the constraint support to obtain the updated spatial complex amplitude distribution.

[0096] Among them, constraint support is the constraint condition for spatially constraining the spatial complex amplitude distribution corresponding to the target object. Specifically, it may include the pixel positions of a preset number of pixels with the largest absolute value of pixel value in the spatial amplitude distribution corresponding to the spatial complex amplitude distribution.

[0097] Specifically, after the terminal recovers the spatial complex amplitude distribution of the target object in the airspace, it determines the constraint support based on the spatial complex amplitude distribution, and applies dynamic spatial constraints to the spatial complex amplitude distribution based on the determined constraint support to update the spatial complex amplitude distribution and obtain the updated spatial complex amplitude distribution.

[0098] In one embodiment, spatial constraints are applied to the spatial complex amplitude distribution based on constraint supports. Specifically, this can be achieved by: selecting pixels from the spatial complex amplitude distribution that correspond to the pixel positions in the constraint supports, keeping the pixel values ​​of the selected pixels unchanged, and setting the pixel values ​​of the remaining pixels in the spatial complex amplitude distribution to zero, thereby obtaining the updated spatial complex amplitude distribution.

[0099] In the above embodiments, the constraint support is dynamically determined based on the recovered spatial complex amplitude distribution, and the spatial complex amplitude distribution is spatially constrained based on the dynamically determined constraint support, so as to obtain a more accurate spatial complex amplitude distribution, so that the target phase distribution of the intensity point spread function in the frequency domain can be accurately recovered based on the updated spatial complex amplitude distribution.

[0100] In one embodiment, obtaining constraint support based on the spatial complex amplitude distribution includes: obtaining the spatial amplitude distribution corresponding to the target object based on the spatial complex amplitude distribution; and obtaining constraint support by selecting a preset number of pixel positions from the spatial amplitude distribution based on the pixel value of each pixel.

[0101] The preset quantity can be customized according to actual needs. For example, it can be determined by the number of pixels of non-zero elements in the object image corresponding to the target object. Specifically, the estimated value of the number of pixels of non-zero elements in the object image can be used as the preset quantity. Alternatively, the number of pixels of non-zero elements in the plaintext autocorrelation distribution corresponding to the target object can be determined, and the preset quantity value can be selected from 1 / 6 to 1 / 4 of the determined number of pixels. For example, if the number of pixels of non-zero elements in the plaintext autocorrelation distribution is 24000, then the preset quantity can be selected from the interval [4000, 6000]. For example, the preset quantity can be determined to be 5000.

[0102] Specifically, the terminal performs amplitude extraction processing on the recovered spatial complex amplitude distribution to obtain the spatial amplitude distribution corresponding to the target object. It then traverses the pixel values ​​of each pixel in the spatial amplitude distribution and selects a preset number of pixels with the largest absolute values ​​from the spatial amplitude distribution based on the traversed pixel values. Finally, it obtains constraint support based on the pixel positions of the selected pixels in the spatial amplitude distribution.

[0103] In the above embodiments, the constraint support of the spatial constraint is dynamically determined according to the spatial complex amplitude distribution corresponding to the target object, so as to perform spatial constraint on the recovered spatial complex amplitude distribution based on the dynamically determined constraint support, and obtain a more accurate spatial complex amplitude distribution.

[0104] In one embodiment, step 106 includes: recovering the target spectrum distribution in the frequency domain based on the speckle spectrum distribution and the target phase distribution corresponding to the target speckle pattern in the frequency domain; performing a Fourier transform on the target spectrum distribution to reconstruct the object image corresponding to the target object.

[0105] Specifically, the terminal performs a Fourier transform on the target speckle map to obtain the speckle spectrum distribution corresponding to the target speckle map in the frequency domain. Based on the target phase distribution corresponding to the intensity point spread function recovered from the target speckle map in the frequency domain, and the target spectrum distribution corresponding to the target object in the frequency domain, the terminal recovers the target spectrum distribution corresponding to the target object in the frequency domain. Then, it performs a Fourier transform on the target spectrum distribution to complete the reconstruction of the target object and obtain the object image corresponding to the target object.

[0106] In the above embodiments, the target phase distribution in the frequency domain corresponding to the intensity point spread function accurately recovered from the target speckle map is obtained, and the target speckle map can accurately reconstruct the target object to obtain the corresponding object image.

[0107] like Figure 2 As shown, this diagram illustrates the principle of iteratively processing a target speckle map based on a preset iterative phase retrieval algorithm to obtain the target phase distribution corresponding to the intensity point spread function in the frequency domain. (Refer to...) Figure 2 The target speckle pattern I formed by imaging the target object through the scattering medium under white light illumination is obtained. The target speckle pattern I is subjected to Fourier transform to obtain the corresponding speckle spectrum distribution FT{I}. The speckle spectrum distribution FT{I} is processed by taking the phase and amplitude respectively to obtain the speckle phase distribution Phase{FT{I}} and speckle amplitude distribution |FT{I}|. According to the above formula (6), the speckle amplitude distribution |FT{I}| is approximated as the first amplitude distribution |FT{O}| of the target object in the frequency domain during the iterative phase recovery process. A phase image Phase{FT{h}}1 is randomly generated as the initial guess of the key information (phase distribution) of the intensity point spread function h, that is, as the current phase distribution of the intensity point spread function in the first (first) iteration of the iterative phase recovery algorithm, so as to initialize the phase distribution of the intensity point spread function in the iterative phase recovery process.

[0108] Taking the k-th iteration of the iterative phase retrieval algorithm as an example, the single iteration process is illustrated as follows: Obtain the current phase distribution of the intensity point spread function in the frequency domain in the k-th iteration (the current iteration): Phase{FT{h}} k According to the current phase distribution Phase{FT{h}} k The first phase distribution of the target object in the frequency domain, Phase{FT{I}}, is calculated using the above formula (7) (the phase difference between the speckle phase distribution and the phase distribution of the intensity point spread function in the frequency domain is approximately equal to the phase distribution of the target object in the frequency domain). k Based on the first amplitude distribution |FT{O}| and the first phase distribution Phase{FT{O}} of the target object in the frequency domain k The corresponding first spectral distribution FT{O is obtained. k}, for the first spectral distribution FT{O k Performing an inverse Fourier transform yields the spatial complex amplitude distribution O of the target object in the spatial domain. k Based on the spatial complex amplitude distribution O k Determine the constraint support S k Based on constraint support S k For the spatial complex amplitude distribution O k The updated spatial complex amplitude distribution O' is obtained by performing spatial constraints. k For the updated spatial complex amplitude distribution O' k Perform a Fourier transform to obtain the second spectral distribution FT{O' of the target object.k}, for the second spectral distribution FT{O' k Phase processing is performed to obtain the second phase distribution of the target object in the frequency domain.

[0109] like Figure 2 As shown, after recovering the second phase distribution of the target object in the frequency domain, the first phase distribution of the target object in the frequency domain in the current iteration can be updated according to the second phase distribution. Based on the second phase distribution (updated first phase distribution) and the speckle phase distribution, the current phase distribution of the intensity point spread function in the current iteration (kth iteration) is updated according to the above formula (7). k If the iteration stopping condition is met, then the updated current phase distribution Phase{FT{h}} will be updated. k The target phase distribution, corresponding to the intensity point spread function in the frequency domain, is used as the output of the iterative phase retrieval algorithm. If the iteration stopping condition is not met, the updated current phase distribution is then used (Phase{FT{h}}). k The current phase distribution corresponding to the intensity point spread function in the next iteration (the (k+1)th iteration) (i.e., the input Phase{FT{h}} for the next iteration) k+1 ).

[0110] In the above embodiments, through the iterative phase retrieval algorithm, scattering imaging under white light illumination can be accurately achieved without intruding into the light scattering imaging system or acquiring multiple frames of target speckle maps for noise suppression. That is, the key information of the intensity point spread function h of the system (target phase distribution Phase{FT{h}}) can be accurately recovered from the target speckle map corresponding to the target object. Then, based on the target speckle map I and the recovered target phase distribution Phase{FT{h}}, deconvolution processing is performed according to the above formula (5) to reconstruct the target object to be imaged. It can be understood that for each light scattering imaging system, after recovering the target phase distribution of the intensity point spread function of the system in the frequency domain based on a single target speckle map corresponding to a single target object, the target phase distribution can be directly used to reconstruct subsequent target speckle maps under the light scattering imaging system, which has the convenient characteristic of "once and for all".

[0111] In one embodiment, the white light illumination scattering imaging method provided in this application realizes "single exposure", "non-invasive", and "broad spectrum" imaging through the scattering medium, further expanding the application scenarios of light scattering imaging technology.

[0112] Figure 3This is a schematic diagram illustrating the effect of reconstructing the target object based on a single target speckle pattern formed after imaging the target object through a scattering medium under white light illumination, and the phase distribution of the system's intensity point spread function, in one embodiment. (Refer to...) Figure 3 Label a represents the target speckle pattern formed after the target object is imaged through the scattering medium under white light illumination. Label b represents the intensity point spread function measured in advance through the input point of the intrusive light scattering imaging system. Label c represents the object image corresponding to the target object reconstructed by deconvolution processing according to the above formula (3) based on the target speckle pattern corresponding to label a and the intensity point spread function corresponding to label b. Label d represents the object image corresponding to the target object reconstructed by deconvolution processing according to the above formula (5) based on the phase distribution of the target speckle pattern corresponding to label a and the intensity point spread function corresponding to label b. Label e represents the actual object image corresponding to the target object, which is the amplitude-type object function hidden behind the scattering medium.

[0113] based on Figure 3 As can be seen from the results, the object image reconstructed based on the phase distribution of the intensity point spread function and the target speckle map has a higher signal-to-noise ratio than the object image reconstructed based on the intensity point spread function and the target speckle map.

[0114] Figure 4 This is a schematic diagram of the architecture of a light scattering imaging system in one embodiment. (Refer to...) Figure 4 A white light source is used as the system's light source to illuminate the entire light scattering imaging system. The scattering medium is placed between the object plane and the image plane, and the image acquisition device is placed on the image plane. Light carrying object information is scattered by the scattering medium and forms speckle patterns on the image plane. The image acquisition device records the intensity distribution of these speckle patterns to obtain the target speckle map corresponding to the target object. The image acquisition device is exemplified by a camera, but is not limited to a single camera.

[0115] To demonstrate the effectiveness of the white light illumination scattering imaging method provided in this application, a series of optical experiments and simulation reconstructions were conducted. The light scattering imaging system involved in the optical experiments is as follows: an LED white light source with a spectral range of 400-700 nm was used as the system light source; frosted glass was used as the scattering medium and placed 34.5 cm behind the target object at a distance from the object plane; a high dynamic range CMOS camera was used as the image acquisition device and placed 10 cm behind the scattering medium on the image plane. It is understood that the light emitted by the white light source typically has a certain divergence angle, the angle of which is not specifically limited here.

[0116] Figure 5 This is a schematic diagram illustrating the effect of reconstructing a target object using a white light illumination-based scattering imaging method in one embodiment. (Refer to...) Figure 5Label 'a' represents the target speckle pattern corresponding to the target object in the aforementioned light scattering imaging system. Label 'b' represents the object image (object information) reconstructed based on the scattering imaging method provided in this application (recovering the target phase distribution corresponding to the intensity point spread function in the frequency domain based on the target speckle pattern corresponding to label 'a', and reconstructing the target object based on the recovered target phase distribution and the target speckle pattern). Label 'c' represents the object image corresponding to the other target object reconstructed based on the target phase distribution recovered from the target speckle pattern corresponding to label 'a' and the target speckle patterns corresponding to other target objects.

[0117] Based on the above Figure 5 It is known that the white light illumination scattering imaging method provided in this application can accurately reconstruct the object image corresponding to the target object. This shows that the key information (target phase information) of the intensity point spread function estimated from the target speckle pattern can accurately characterize the light scattering imaging system and has a certain degree of robustness.

[0118] It should be understood that, although Figure 1 and Figure 3 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 and Figure 3 At least some of the steps in the process may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0119] In one embodiment, such as Figure 6 As shown, a white light illumination scattering imaging device 600 is provided, comprising: an acquisition module 601, an iterative recovery module 602, and a reconstruction module 603, wherein:

[0120] The acquisition module 601 is used to acquire the target speckle pattern formed after the target object is imaged through a scattering medium under white light illumination;

[0121] The iterative recovery module 602 is used to iteratively process the target speckle pattern using a preset iterative phase recovery algorithm to obtain the target phase distribution corresponding to the intensity point spread function in the frequency domain.

[0122] The reconstruction module 603 is used to perform deconvolution processing based on the target speckle map and the target phase distribution to complete the reconstruction of the target object.

[0123] In one embodiment, the iterative recovery module 602 is further configured to obtain the current phase distribution of the intensity point spread function in the frequency domain; recover the spatial complex amplitude distribution corresponding to the target object based on the target speckle map and the current phase distribution; apply spatial constraints to the spatial complex amplitude distribution to obtain an updated spatial complex amplitude distribution; update the current phase distribution based on the updated spatial complex amplitude distribution and the target speckle map, and return to recovering the spatial complex amplitude distribution corresponding to the target object based on the target speckle map and the current phase distribution, until the iteration stop condition is met, and determine the updated current phase distribution as the target phase distribution corresponding to the intensity point spread function in the frequency domain.

[0124] In one embodiment, the iterative recovery module 602 is further configured to obtain the corresponding speckle amplitude distribution and speckle phase distribution based on the target speckle map; obtain the first phase distribution of the target object in the frequency domain based on the speckle phase distribution and the current phase distribution; recover the first spectral distribution corresponding to the target object based on the speckle amplitude distribution and the first phase distribution; and perform an inverse Fourier transform on the first spectral distribution to obtain the spatial complex amplitude distribution corresponding to the target object.

[0125] In one embodiment, the iterative recovery module 602 is further configured to perform an inverse Fourier transform on the updated spatial complex amplitude distribution to obtain a second spectral distribution corresponding to the target object; obtain a second phase distribution of the target object in the frequency domain based on the second spectral distribution; and update the current phase distribution based on the speckle phase distribution and the second phase distribution.

[0126] In one embodiment, the iterative recovery module 602 is further configured to obtain constraint support based on the spatial complex amplitude distribution; and to perform spatial constraints on the spatial complex amplitude distribution based on the constraint support to obtain an updated spatial complex amplitude distribution.

[0127] In one embodiment, the iterative recovery module 602 is further configured to obtain the spatial amplitude distribution corresponding to the target object based on the spatial complex amplitude distribution; and to obtain constraint support by selecting a preset number of pixel positions from the spatial amplitude distribution based on the pixel value of each pixel.

[0128] In one embodiment, the reconstruction module 603 is further configured to recover the target spectrum distribution in the frequency domain corresponding to the target object based on the speckle spectrum distribution and the target phase distribution corresponding to the target speckle pattern in the frequency domain; and to reconstruct the object image corresponding to the target object by performing a Fourier transform on the target spectrum distribution.

[0129] Specific limitations regarding the white light illumination scattering imaging device can be found in the limitations of the white light illumination scattering imaging method described above, and will not be repeated here. Each module in the aforementioned white light illumination scattering imaging device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0130] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a white light illumination scattering imaging method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0131] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0132] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps in the method embodiments.

[0133] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the various method embodiments.

[0134] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0135] 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.

[0136] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A white light illumination scattering imaging method, characterized in that, The method includes: Acquire the speckle pattern of the target object after imaging through a scattering medium under white light illumination; Obtain the current phase distribution of the intensity point spread function in the frequency domain; The corresponding speckle amplitude distribution and speckle phase distribution are obtained based on the target speckle pattern. Based on the speckle phase distribution and the current phase distribution, the first phase distribution of the target object in the frequency domain is obtained; Based on the speckle amplitude distribution and the first phase distribution, the first spectral distribution corresponding to the target object is recovered; Performing an inverse Fourier transform on the first spectral distribution yields the spatial complex amplitude distribution corresponding to the target object; The spatial complex amplitude distribution is spatially constrained to obtain the updated spatial complex amplitude distribution; Perform an inverse Fourier transform on the updated spatial complex amplitude distribution to obtain the second spectral distribution corresponding to the target object; The second phase distribution of the target object in the frequency domain is obtained based on the second spectral distribution. The current phase distribution is updated based on the speckle phase distribution and the second phase distribution, and the process returns to the step of restoring the spatial complex amplitude distribution corresponding to the target object based on the target speckle pattern and the current phase distribution, until the iteration stop condition is met, and the updated current phase distribution is determined as the target phase distribution corresponding to the intensity point spread function in the frequency domain. The target object is reconstructed by performing deconvolution processing based on the target speckle map and the target phase distribution.

2. The method according to claim 1, characterized in that, The step of spatially constraining the spatial complex amplitude distribution to obtain the updated spatial complex amplitude distribution includes: The constrained support is obtained based on the spatial complex amplitude distribution; Based on the aforementioned constraint support, the spatial complex amplitude distribution is subjected to spatial constraints to obtain an updated spatial complex amplitude distribution.

3. The method according to claim 2, characterized in that, The constraint support obtained based on the spatial complex amplitude distribution includes: The spatial amplitude distribution corresponding to the target object is obtained based on the spatial complex amplitude distribution. Constraint support is obtained by selecting a preset number of pixel positions from the spatial amplitude distribution based on the pixel value of each pixel.

4. The method according to claim 1, characterized in that, The step of deconvolution processing based on the target speckle map and the target phase distribution to reconstruct the target object includes: Based on the speckle spectrum distribution in the frequency domain corresponding to the target speckle pattern and the target phase distribution, the target spectrum distribution in the frequency domain corresponding to the target object is recovered. A Fourier transform is performed on the target spectral distribution to reconstruct the object image corresponding to the target object.

5. A white light illumination scattering imaging device, characterized in that, The device includes: The acquisition module is used to acquire the target speckle pattern formed after the target object is imaged through a scattering medium under white light illumination; The iterative recovery module is used to obtain the current phase distribution of the intensity point spread function in the frequency domain; obtain the corresponding speckle amplitude distribution and speckle phase distribution based on the target speckle map; obtain the first phase distribution of the target object in the frequency domain based on the speckle phase distribution and the current phase distribution; recover the first spectral distribution of the target object based on the speckle amplitude distribution and the first phase distribution; perform an inverse Fourier transform on the first spectral distribution to obtain the spatial complex amplitude distribution of the target object; apply spatial constraints to the spatial complex amplitude distribution to obtain an updated spatial complex amplitude distribution; perform an inverse Fourier transform on the updated spatial complex amplitude distribution to obtain the second spectral distribution of the target object; obtain the second phase distribution of the target object in the frequency domain based on the second spectral distribution; update the current phase distribution based on the speckle phase distribution and the second phase distribution, and return to the step of recovering the spatial complex amplitude distribution of the target object based on the target speckle map and the current phase distribution to continue execution until the iteration stop condition is met, and determine the updated current phase distribution as the target phase distribution of the intensity point spread function in the frequency domain; The reconstruction module is used to perform deconvolution processing based on the target speckle map and the target phase distribution to complete the reconstruction of the target object.

6. The apparatus according to claim 5, characterized in that, The iterative recovery module is used to: obtain constraint support based on the spatial complex amplitude distribution; and perform spatial constraints on the spatial complex amplitude distribution based on the constraint support to obtain an updated spatial complex amplitude distribution.

7. The apparatus according to claim 6, characterized in that, The iterative recovery module is used to: obtain the spatial amplitude distribution corresponding to the target object based on the spatial complex amplitude distribution; and obtain constraint support by selecting a preset number of pixel positions from the spatial amplitude distribution based on the pixel value of each pixel.

8. The apparatus according to claim 5, characterized in that, The reconstruction module is used to: recover the target spectrum distribution in the frequency domain based on the speckle spectrum distribution corresponding to the target speckle pattern in the frequency domain and the target phase distribution; and perform Fourier transform on the target spectrum distribution to reconstruct the object image corresponding to the target object.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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