A phase unwrapping method and system for adaptive path search based on quality map

Abstract

The application discloses a phase solving method of an adaptive path search algorithm based on a quality map, relates to the technical field of path planning, and comprises the following steps: firstly, constructing a multi-index quality map by fusing phase derivative variance, local coherence and gradient consistency; secondly, performing threshold segmentation on the quality map, extracting a high-quality area, and sorting the high-quality area according to priority; thirdly, adopting an adaptive path search algorithm, comprehensively considering a reference step length, a quality factor, a distance factor and a reliability factor to solve a search path problem, and simultaneously optimizing the search path problem in combination with dynamic constraints; and fourthly, performing phase solving along the path, returning a success rate of the solved phase in combination with a feedback system, and dynamically adjusting the step length. The application solves the problems of low precision and low efficiency of path search in the prior art.

Description

Technical Field

[0001] This invention belongs to the field of path planning technology, specifically relating to a phase unfolding method and system for adaptive path search based on quality graphs. Background Technology

[0002] Structured light 3D measurement is a method for measuring the 3D surface of objects that has emerged in recent years. It boasts advantages such as non-contact operation, speed and convenience, dense point cloud coverage, high accuracy, and low cost, and has been widely applied in industrial inspection, consumer electronics, virtual reality, augmented reality, cultural relic preservation, and medical devices. A structured light vision measurement system generally consists of a projector, a camera, a computer, and the object being measured. The basic process includes projecting a series of computer-generated phase-shift patterns onto the surface of the object, acquiring images of the object's surface via a camera, performing phase calculation and phase-to-height mapping on the acquired images, and combining the intrinsic and extrinsic parameters of the camera and projector to perform 3D reconstruction of the object's surface. The phase unfolding algorithm is the core of the entire reconstruction process; the phase obtained directly from the phase map will be... The phase between these points is called a wrapped phase. Unwrapping is required, which necessitates a suitable phase unwrapping algorithm to ensure the phase is a continuous, natural range. For an ideal wrapped phase diagram, phase unwrapping is path-independent.

[0003] However, in practice, path search accuracy and efficiency are low due to factors such as local shadows, low stripe modulation quality, low texture areas, irregular surface brightness, edge discontinuities, and undersampling. Summary of the Invention

[0004] The purpose of this invention is to provide a phase unrolling method and system for adaptive path search based on quality graphs, which can solve the problem of low accuracy and efficiency of path search in the prior art. To solve the above-mentioned technical problems, the present invention is implemented as follows: In a first aspect, embodiments of the present invention provide a phase unrolling method based on adaptive path search of a quality map, comprising: S1. Acquire the raw phase image; S2. Construct a quality map of the original phase image based on the phase derivative variance, local coherence and gradient consistency indices to obtain a multi-index quality map. S3. Based on OTU, perform region segmentation on the multi-index quality map to obtain multiple segmentation regions; S4. Select a starting point from multiple segmented regions; S5. Calculate the dynamic step size constraint to obtain the maximum allowable step size; S6. Explore multiple segmented regions based on the starting point and the maximum allowed step size to find a reliable path; S7. When a reliable path is found, perform phase expansion along the reliable path and determine whether all segmented regions are connected. If they are connected, output the expanded phase; otherwise, return to step S4. When no reliable path is found, perform constraint relaxation and single-step expansion processing, and return to step S5.

[0005] As an optional implementation of the first aspect of this application, the phase derivative variance The mathematical expression is: ; The formula for calculating the phase gradient in the x / y directions is as follows: ; in, This represents the first-order phase difference in the x / y directions. This represents the wrapped phase obtained from the original phase. This represents the variance within a local window. The window size affects sensitivity, and a suitable window size needs to be determined based on the actual results. Local coherence The mathematical expression is: ; in, This indicates the total number of pixels within the window. This represents a local calculation window. Indicates the phase difference between adjacent pixels; The gradient consistency index The mathematical expression is: ; in, Represents the global gradient. Represents the global gradient mean. Represents the Euclidean distance, used to quantify the degree of gradient anomalies; The multi-index quality chart The mathematical expression is: ; in, , Indicates the weight.

[0006] As an optional implementation of the first aspect of this application, the process of obtaining multiple segmented regions by region segmentation of a multi-index quality map based on OTU includes: Calculate the number of low-quality region pixels, the number of high-quality region pixels, the percentage of low-quality region pixels, the percentage of high-quality region pixels, the average gray level of low-quality region, and the average gray level of high-quality region for each candidate threshold. Calculate the current inter-class variance corresponding to the current candidate threshold. When the current inter-class variance is greater than the preset maximum inter-class variance, update the value of the maximum inter-class variance to the current inter-class variance, and assign the current candidate threshold to the final threshold. The image is segmented into low-quality and high-quality regions based on the final threshold.

[0007] As an optional implementation of the first aspect of this application, the process of calculating dynamic step size constraints to obtain the maximum allowable step size includes: establishing a dynamic step size constraint function, substituting a reference step size into the dynamic step size constraint function, and obtaining the maximum allowable step size, wherein the mathematical expression for the maximum allowable step size is: ; in, Indicates the maximum allowed step size. Indicates the reference step size. This represents the mass value at the current starting point. This represents the distance from the current point to the target area. This indicates the preset maximum effective distance. This represents the estimated minimum quality value of the path. This represents the Sigmoid activation function.

[0008] As an optional implementation of the first aspect of this application, the process of exploring multiple segmented regions based on a starting point and a maximum permissible step size to find a reliable path includes: ; ; in, The unwrapping phase represents the endpoint of the path. Indicates the original phase at the starting point of the path. This indicates the phase difference between adjacent points along the path. Indicates the starting point of the path. Indicates the endpoint of the path. Represents a sequence of path nodes. Indicates path node Adjacent nodes, Indicates from node Search for paths among neighboring nodes. Represented as a wrapper operator, it is used to map the phase to... interval, Represents a node The original phase, Represents a node The original phase, the path node set generated by the adaptive search algorithm Indicates the number of path segments.

[0009] Secondly, embodiments of the present invention provide a phase unrolling system based on quality map adaptive path search, comprising: The raw phase image acquisition module is used to acquire raw phase images; The multi-index quality map construction module is used to construct a quality map of the original phase image based on the phase derivative variance, local coherence and gradient consistency indices to obtain a multi-index quality map. The region segmentation module is used to segment the multi-index quality map based on the OTU to obtain multiple segmentation regions; The path exploration module is used to select a starting point from multiple segmented regions, calculate dynamic step size constraints to obtain the maximum allowable step size, and explore multiple segmented regions based on the starting point and the maximum allowable step size to find a reliable path; The phase unrolling module is used to unroll the phase along a reliable path when a reliable path is found, and to determine whether all segmented regions are connected. If they are connected, the unrolled phase is output; otherwise, the dynamic step size constraint is recalculated to obtain the maximum allowable step size. When no reliable path is found, the module re-explores multiple segmented regions based on the starting point and the maximum allowable step size to find a reliable path.

[0010] Thirdly, embodiments of the present invention provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method as described in the first aspect.

[0011] Fourthly, embodiments of the present invention provide a readable storage medium on which a program or instructions are stored, and when the program or instructions are executed by a processor, the steps of the method as described in the first aspect are implemented.

[0012] Compared with existing technologies, the phase unfolding method based on quality graph adaptive path search proposed in this invention has the following advantages: This method integrates multiple indicators to construct a quality graph and adopts the principle of dynamic balance, thus balancing the computational complexity, accuracy, and efficiency of the search path. In terms of path search, this method comprehensively combines multiple indicators to adaptively partition the optimal path, proposing a novel solution that integrates multi-indicator quality assessment and adaptive search. By dynamically constraining the path growth process, it significantly improves the phase solution accuracy in noisy environments while ensuring computational efficiency. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating a phase unrolling method based on quality map adaptive path search provided in the first embodiment of the present invention; Figure 2 This diagram illustrates the internal structure of a phase unrolling system based on quality graph adaptive path search, as provided in the second embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0016] The phase unrolling method and system for adaptive path search based on quality graphs provided by the present invention will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0017] Example 1 Please see Figure 1 The figure shows an overall flowchart of a phase unrolling method based on quality graph adaptive path search provided by the present invention, which includes steps S1 to S7.

[0018] S1. Acquire the original phase image.

[0019] Specifically, the process of acquiring the original phase image in this invention includes: projecting a sinusoidal grating stripe or Gray code image onto the surface of the object being measured using a projector, and then acquiring an image of the surface of the object being measured using a camera. This image is the original phase image.

[0020] S2. Construct a quality map of the original phase image based on the phase derivative variance, local coherence and gradient consistency indices to obtain a multi-index quality map.

[0021] Among these metrics, phase derivative variance primarily detects local phase abrupt changes, local coherence is used to assess the continuity of the phase field, and gradient consistency reflects the structural characteristics of the stripe pattern. Before constructing a quality map by integrating these metrics, this invention preprocesses the original image by adding a median filtering algorithm to eliminate some of the noise influence, serving as the basis for constructing a high-quality image.

[0022] Specifically, the mathematical expression for the variance of the phase derivative is: ; in, Represents the variance of the phase derivative. () represents the variance within a local window. The window size affects sensitivity, and a suitable window size needs to be determined based on actual results. and They represent in and The first-order phase difference in the direction.

[0023] The formula for calculating the phase gradient in the x / y directions is as follows: ; in Indicates in direction and Phase first-order difference in the direction, This represents the wrapped phase obtained from the original phase.

[0024] The mathematical expression for local coherence is: ; in, Indicates local coherence. This indicates the total number of pixels within the window. Indicates the phase difference between adjacent pixels. This represents a local calculation window.

[0025] The mathematical expression for the gradient consistency index is: ; in, This represents the gradient consistency index. This represents the gradient vector of the current pixel. Represents the global gradient mean. This represents the Euclidean distance, used to quantify the degree of gradient anomalies.

[0026] Furthermore, the specific process of obtaining the multi-index quality map includes: First, using a weighted fusion strategy, the phase derivative variance, local coherence, and gradient consistency indices are weighted and calculated to obtain the multi-index quality map to be processed. The mathematical expression for the multi-index quality map to be processed is: ; in, This represents the multi-index quality chart to be processed. , , Indicates the weight.

[0027] Furthermore, the aforementioned multi-index quality chart to be processed... Filtering and normalization are performed sequentially to obtain a multi-index quality map.

[0028] S3. Based on OTU, perform region segmentation on the multi-index quality map to obtain multiple segmented regions.

[0029] Specifically, this invention divides the obtained quality map into different regions according to the quality values ​​from high to low based on the obtained quality map information. This addresses the limitations of bimodal or multimodal distributions in phase quality maps, the limitations of manual thresholding, and the lack of coherence between regions. Based on the distribution pattern of the quality map histogram, the region segmentation threshold is automatically obtained using the OTU method (Great Law Method).

[0030] This invention employs a single-threshold segmentation algorithm based on the OTU method for quality map region segmentation. The core idea of ​​this algorithm is to automatically find the optimal threshold. quality diagram Classified as a low-quality area ( ) and high-quality areas ( The system is divided into two categories, minimizing the differences within each category and maximizing the differences between categories. The specific implementation process includes steps S31 to S33.

[0031] S31. Calculate the number of low-quality pixels for each candidate threshold. High-quality area pixel count Low-quality area pixel ratio High-quality area pixel ratio Average gray level of low-quality areas High-quality area average gray level ; Specifically, the calculation process needs to satisfy the following conditions: conservation of total number of pixels, conservation of weight normalization, and conservation of mean. The conservation condition for total number of pixels is as follows: ; The condition for the conservation of weight normalization is: ; The condition for the conservation of the mean is: ; in, Indicates the number of rows in the image. Indicates the number of columns in the image. Represents the total number of pixels in the image. This represents the overall average gray level of the image.

[0032] S32. Calculate the current inter-class variance corresponding to the current candidate threshold. When the current inter-class variance is greater than the preset maximum inter-class variance, update the value of the maximum inter-class variance to the current inter-class variance, and assign the current candidate threshold to the final threshold. Specifically, the current inter-class variance corresponding to the current candidate threshold. The equivalent form of inter-class variance is expressed as: ; Furthermore, when the current inter-class variance is greater than the preset maximum inter-class variance, the process of updating the value of the maximum inter-class variance to the current inter-class variance and assigning the current candidate threshold as the final threshold includes: Determine the current inter-class variance corresponding to the current candidate threshold. Compared with the preset maximum inter-class variance The size relationship, if Then update ,in, Indicates the current candidate threshold. This represents the final threshold.

[0033] S33. Based on the final threshold, segment the image into low-quality regions and high-quality regions.

[0034] Specifically, based on the final threshold The image is segmented into low-quality regions, expressed as: Low-quality regions: ,in, These represent the horizontal and vertical coordinates, respectively.

[0035] Furthermore, based on the final threshold The image is segmented into high-quality regions, expressed as follows: .

[0036] S4. Select a starting point from the plurality of segmented regions.

[0037] Specifically, this invention selects a starting point to gradually spread from a high-quality region to a low-quality region, selects a starting point in each segmented region, and explores paths based on that starting point.

[0038] S5. Calculate the dynamic step size constraint to obtain the maximum allowable step size.

[0039] It is worth noting that this invention comprehensively considers four factors affecting step size—baseline step size, quality factor, distance factor, and reliability factor—to calculate the maximum allowable step size. Among them, the baseline step size controls the basic range scale of path search and determines the overall exploration capability of the algorithm. The quality factor is used to evaluate the reliability of the starting point; the higher the reliability, the farther the exploration distance is allowed, preventing the error propagation of low-quality starting points. The distance factor tightens constraints when approaching the target (to prevent overshoot) and relaxes constraints when far away (to accelerate connection). The reliability factor is used to evaluate the penalty system; low-quality areas are prohibited from selection, and paths with reliable segments throughout are preferred.

[0040] Specifically, the process of obtaining the maximum allowable step size includes: establishing a dynamic step size constraint function, substituting the baseline step size into the dynamic step size constraint function, and obtaining the maximum allowable step size. The mathematical expression for the maximum allowable step size is: ; in, Indicates the maximum allowed step size. Indicates the reference step size. This represents the mass value at the current starting point. Represents the distance factor. This represents the reliability factor.

[0041] The mathematical expression for the quality factor is: ; in, This represents the current point quality value. This indicates the maximum mass.

[0042] When the starting point quality is high ( When the starting point quality is low, it allows exploration to extend further. When using this method, the step size needs to be strictly limited.

[0043] Among them, distance factor The mathematical expression is: ; in, This represents the Sigmoid activation function. , Control the steepness, This represents the distance from the current point to the target area. This indicates the preset maximum effective distance. Among them, reliability factor The mathematical expression is: ; in, This represents the estimated minimum quality value of the path.

[0044] S6. Explore multiple segmented regions based on the starting point and the maximum allowed step size to find a reliable path.

[0045] Specifically, the maximum allowed step size is obtained after step S5 is completed. Subsequently, during the process of finding a reliable path in step S6, the present invention uses the maximum permissible step size. Using the radius as the starting point, search within the high-quality and low-quality regions obtained in step S33 for a reliable path with the highest overall reliability (i.e., the lowest quality value). If this reliable path is found, proceed to step S7; otherwise, continue exploring multiple segmented regions based on the starting point and maximum allowable step size to find a reliable path until a reliable path is found before proceeding to step S7. The process of exploring multiple segmented regions based on the starting point and maximum allowable step size to find a reliable path includes: ; ; in, The unwrapping phase represents the endpoint of the path. Indicates the original phase at the starting point of the path. This indicates the phase difference between adjacent points along the path. Indicates the starting point of the path. Indicates the endpoint of the path. Represents a sequence of path nodes. Indicates path node Adjacent nodes, Indicates from node Search for paths among neighboring nodes. Represented as a wrapper operator, used to map the phase to... interval, Represents a node The original phase, Represents a node The original phase, the path node set generated by the adaptive search algorithm Indicates the number of path segments.

[0046] Specifically, this invention establishes a starting point and a basic step size for initial path exploration. The starting point is generally selected from a high-quality region, gradually spreading towards a low-quality region. The value of the basic step size can be set arbitrarily; for a 1280×720 resolution, it can be set between 5 and 25 pixels. Based on the above influencing factor equation, the final step size is obtained, and then its performance is evaluated. A success threshold is set; exceeding this threshold indicates a high success rate. This also requires constraints: the baseline step size needs to be increased, and the smallest step size along the path direction is taken as the baseline step size. If the success rate is less than this success threshold, it is considered a low success rate, and its baseline step size needs to be reduced, with the largest step size along the path direction taken as the baseline step size. This constrains the step size to a stable region, taking into account both computational efficiency and computation time.

[0047] S7. When a reliable path is found, perform phase expansion along the reliable path and determine whether all segmented regions are connected. If they are connected, output the expanded phase; otherwise, return to step S4. When no reliable path is found, perform constraint relaxation and single-step expansion processing, and return to step S5.

[0048] Specifically, the process of relaxing constraints and single-step expansion includes: after executing step S5, if the quality values ​​of the found paths are all greater than a certain set threshold (this threshold is the local minimum quality value), it means that these paths are unreliable. Then, it is necessary to return to step S5 to recalculate the baseline step size, dynamic step size constraint, quality factor, distance factor, and reliability factor, and repeat the operations from S4 to S5. After obtaining the maximum allowable step size again, a reliable path is found, or a reliable path is found directly with the baseline step size as the radius. Example 2 Please see Figure 2 This represents an internal structure diagram of a phase unrolling system based on quality map adaptive path search provided in the second embodiment of the present invention, including: The raw phase image acquisition module 100 is used to acquire raw phase images; The multi-index quality map construction module 200 is used to construct a quality map of the original phase image based on the phase derivative variance, local coherence and gradient consistency indices to obtain a multi-index quality map. The region segmentation module 300 is used to perform region segmentation on the multi-index quality map based on the OTU to obtain multiple segmentation regions; The path exploration module 400 is used to select a starting point from multiple segmented regions, calculate dynamic step size constraints to obtain the maximum allowable step size, and explore multiple segmented regions based on the starting point and the maximum allowable step size to find a reliable path; The phase unrolling module 500 is used to perform phase unrolling along a reliable path when a reliable path is found, and to determine whether all segmented regions are connected. If they are connected, the unrolled phase is output; otherwise, the dynamic step size constraint is recalculated to obtain the maximum allowable step size. When no reliable path is found, the module re-explores multiple segmented regions based on the starting point and the maximum allowable step size to find a reliable path.

[0049] The phase unrolling system for adaptive path search based on quality graphs in this embodiment of the invention can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, a mobile electronic device can refer to a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., while a non-mobile electronic device can refer to a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This embodiment of the invention does not impose specific limitations.

[0050] The phase unrolling system based on quality graph adaptive path search in this embodiment of the invention can represent a device with an operating system. This operating system can represent Android, iOS, or other possible operating systems; this embodiment of the invention does not specifically limit the scope.

[0051] The phase unrolling system based on quality map adaptive path search provided in this embodiment of the invention can achieve... Figure 1 The various processes implemented by the phase unrolling method based on the quality graph adaptive path search in the method embodiment will not be described again here to avoid repetition.

[0052] Optionally, embodiments of the present invention also provide an electronic device, including a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the various processes of the above-described embodiment of the phase unrolling method for adaptive path search based on a quality graph, and can achieve the same technical effect. To avoid repetition, further details are omitted here.

[0053] This invention also provides a readable storage medium storing a program or instructions. When executed by a processor, the program or instructions implement the various processes of the above-described embodiment of the phase unrolling method for adaptive path search based on a quality graph, and achieve the same technical effect. To avoid repetition, this will not be elaborated further here.

[0054] The processor refers to the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0055] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of the present invention is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0056] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0057] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. A phase unrolling method based on adaptive path search using a quality map, characterized in that, include: S1. Acquire the raw phase image; S2. Construct a quality map of the original phase image based on the phase derivative variance, local coherence and gradient consistency indices to obtain a multi-index quality map. The mathematical expression for the variance of the phase derivative is: ； in, Represents the variance of the phase derivative. () represents the variance within a local window. The window size affects sensitivity, and a suitable window size needs to be determined based on actual results. and They represent in and The first-order phase difference in the direction; The formula for calculating the phase gradient in the x / y directions is as follows: ； in, Indicates in direction and Phase first-order difference in the direction, This represents the wrapped phase obtained from the original phase. This represents the variance within a local window. The window size affects sensitivity, and a suitable window size needs to be determined based on the actual results. The mathematical expression for local coherence is: ； in, Indicates local coherence. This indicates the total number of pixels within the window. Indicates the phase difference between adjacent pixels. Represents a local calculation window; The mathematical expression for the gradient consistency index is: ； in, This represents the gradient consistency index. This represents the gradient vector of the current pixel. Represents the global gradient mean. Represents the Euclidean distance, used to quantify the degree of gradient anomalies; The mathematical expression for the multi-index quality chart is: ； in, This represents the multi-index quality chart to be processed. , , Indicates weight; S3. Based on the OTU, the multi-index quality map is segmented to obtain multiple segmentation regions; S4. Select a starting point from the plurality of segmented regions; S5. Establish a dynamic step size constraint function, substitute the baseline step size into the dynamic step size constraint function, and obtain the maximum allowable step size. The mathematical expression for the maximum allowable step size is: ； in, Indicates the maximum allowed step size. Indicates the reference step size. This represents the mass value at the current starting point. This represents the distance from the current point to the target area. This indicates the preset maximum effective distance. This represents the estimated minimum quality value of the path. This represents the Sigmoid activation function; S6. Based on the starting point and the maximum allowed step size, explore the multiple segmented regions to find a reliable path; S7. When a reliable path is found, perform phase expansion along the reliable path and determine whether all the segmented regions are connected. If they are connected, output the expanded phase; otherwise, return to step S4. When no reliable path is found, perform constraint relaxation and single-step expansion processing, and return to step S5.

2. The phase unrolling method based on quality map adaptive path search according to claim 1, characterized in that, The process of segmenting the multi-index quality map based on OTU to obtain multiple segmented regions includes: Calculate the number of low-quality region pixels, the number of high-quality region pixels, the percentage of low-quality region pixels, the percentage of high-quality region pixels, the average gray level of low-quality region, and the average gray level of high-quality region for each candidate threshold. Calculate the current inter-class variance corresponding to the current candidate threshold. When the current inter-class variance is greater than the preset maximum inter-class variance, update the value of the maximum inter-class variance to the current inter-class variance, and assign the current candidate threshold to the final threshold. The image is segmented into low-quality regions and high-quality regions based on the final threshold.

3. The phase unrolling method based on quality map adaptive path search according to claim 1, characterized in that, The process of exploring the multiple segmented regions and finding a reliable path based on the starting point and the maximum allowed step size includes: ； ； in, The unwrapping phase represents the endpoint of the path. Indicates the original phase at the starting point of the path. This indicates the phase difference between adjacent points along the path. Indicates the starting point of the path. Indicates the endpoint of the path. Represents a sequence of path nodes. Indicates path node Adjacent nodes, Indicates from node Search for paths among neighboring nodes. Represented as a wrapper operator, it is used to map the phase to... interval, Represents a node The original phase, Represents a node The original phase, the path node set generated by the adaptive search algorithm Indicates the number of path segments.

4. A phase unrolling system based on adaptive path search of a quality map, characterized in that, A phase unrolling method based on quality map adaptive path search as described in any one of claims 1-3 is applied, wherein the phase unrolling system comprises: The raw phase image acquisition module is used to acquire raw phase images; A multi-index quality map construction module is used to construct a quality map of the original phase image based on the phase derivative variance, local coherence and gradient consistency indices to obtain a multi-index quality map. The region segmentation module is used to perform region segmentation on the multi-index quality map based on the OTU to obtain multiple segmented regions; The path exploration module is used to select a starting point from the multiple segmented regions, calculate dynamic step size constraints to obtain the maximum allowable step size, and explore the multiple segmented regions based on the starting point and the maximum allowable step size to find a reliable path; The phase unrolling module is used to perform phase unrolling along the reliable path when a reliable path is found, and to determine whether all the segmented regions are connected. If they are connected, the unrolled phase is output; otherwise, the dynamic step size constraint is recalculated to obtain the maximum allowable step size. When no reliable path is found, the module re-explores the multiple segmented regions based on the starting point and the maximum allowable step size to find a reliable path.

5. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of a phase unrolling method for adaptive path search based on a quality graph as described in any one of claims 1-3.

6. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of a phase unrolling method for adaptive path search based on a quality graph as described in any one of claims 1-3.

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