CN116681840B - OSMO optimization improvement method for fault reconstruction and storage medium - Google Patents
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Abstract
The application relates to the technical field of volume additive manufacturing, in particular to an OSMO (open-air quality control) optimization and improvement method and a storage medium for fault reconstruction, comprising the following steps of: performing OSMO optimization treatment on the model structure to obtain a final intermediate optimization model and a first intermediate variable of a fault reconstruction target; performing enhancement treatment on the edge area of the final intermediate optimization model to obtain an enhancement optimization model and a second intermediate variable of the fault reconstruction target; and iteratively optimizing the enhanced optimization model and the second intermediate variable based on the steps to obtain an optimal structure of the fault reconstruction target, and performing optimization processing on a target edge area on the basis of an OSMO optimization algorithm to further improve printing precision.
Description
Technical Field
The application belongs to the technical field of volume additive manufacturing, and particularly relates to an OSMO (open-air quality control) optimization and improvement method for fault reconstruction and a storage medium.
Background
The implementation process of the volume additive manufacturing VAM fault reconstruction technology is as follows: during VAM printing, a series of 2D optical patterns (or image sets) are projected into a rotatable container that is filled with photosensitive resin; in the printing process, the projector continuously changes the optical pattern to carry out projection irradiation on the container in the rotating process of the rotating container; and after the photosensitive resin in the container is continuously irradiated for a few seconds to a few minutes, an expected 3D solid structure is formed, and finally the 3D printing model is realized. The ideal procedure for VAM tomographic reconstruction is to obtain an image set from a model structure requiring 3D printing by forward projection calculation, and then obtain a printed structure by projector projection (called back projection). However, a printing structure of low accuracy is obtained due to light scattering. Therefore, it is necessary to perform image optimization calculation before projection by the projector to obtain a printing structure of higher accuracy. Among algorithms for VAM tomographic reconstruction, an Object space model optimization OSMO (Object-space model optimization) optimization algorithm has proven to be a simpler and more accurate tomographic reconstruction optimization improvement method.
The OSMO optimization algorithm method disclosed in the prior art does not process the transition area of the target and the background, namely the target edge area, so that the accuracy of the printing structure obtained by the existing OSMO optimization algorithm can be further improved. Therefore, how to perform optimization processing on the target edge area based on the existing OSMO optimization algorithm, so as to further improve the image set precision and the printing precision becomes a problem to be solved urgently.
Disclosure of Invention
An object of one or more embodiments of the present disclosure is to provide an OSMO optimization improvement method for fault reconstruction, which performs optimization processing on a target edge area based on an OSMO optimization algorithm, so as to further improve printing accuracy.
To solve the above technical problems, one or more embodiments of the present specification are implemented as follows:
in a first aspect, an improved method for optimizing an OSMO for tomographic reconstruction is provided, comprising the steps of: performing OSMO optimization treatment on the model structure to obtain a final intermediate optimization model and a first intermediate variable of a fault reconstruction target; performing enhancement treatment on the edge area of the intermediate optimization model to obtain an enhancement optimization model and a second intermediate variable of the fault reconstruction target; and iteratively optimizing the enhanced optimization model and the second intermediate variable based on the steps to obtain the optimal structure of the fault reconstruction target.
In a second aspect, a storage medium is provided for computer readable storage, the storage medium storing one or more programs which, when executed by one or more processors, implement an OSMO optimization improvement method for tomographic reconstruction as described above.
As can be seen from the technical solutions provided in one or more embodiments of the present disclosure, in the method for improving OSMO optimization for fault reconstruction provided in the embodiments of the present disclosure, an OSMO optimization process is performed on a model structure to obtain a final intermediate optimization model and a first intermediate variable of a fault reconstruction target; performing enhancement treatment on the edge region of the intermediate optimization model to obtain a second intermediate variable of the enhancement optimization model and a fault reconstruction target; and (3) carrying out iterative optimization on the enhanced optimization model and the second intermediate variable based on the steps to obtain an optimal structure of the fault reconstruction target. The most direct evaluation standard of the VAM fault reconstruction precision is the separation degree of a target region and a background region in a fault reconstruction target structure model, and the better the two regions are separated, the higher the fault reconstruction precision is, and the lower the fault reconstruction precision is otherwise. According to the embodiment of the application, the processing steps of edge enhancement are added on the basis of the existing OSMO optimization algorithm, firstly, the processing steps of further enhancing the adjacent area inside the target edge of the model structure and secondly, the processing steps of further weakening the adjacent area outside the target edge of the model structure are carried out, so that the fault reconstruction precision of the target edge area is further improved, and the convergence and accuracy of the iteration of the OSMO optimization algorithm are improved.
Drawings
For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, reference will be made below to the accompanying drawings which are used in the description of one or more embodiments or of the prior art, it being apparent that the drawings in the description below are only some of the embodiments described in the description, from which, without inventive faculty, other drawings can also be obtained for a person skilled in the art.
FIG. 1 is a flow diagram of an OSMO optimization improvement method for fault reconstruction according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an idealized solution for tomographic reconstruction in the prior art;
FIG. 3 is a schematic diagram of a prior art tomographic reconstruction technique;
FIG. 4 is a schematic diagram of an embodiment of an OSMO optimization improvement method for tomographic reconstruction according to an embodiment of the present application;
FIG. 5 is a detailed flow diagram of an OSMO optimization improvement method for fault reconstruction provided in accordance with an embodiment of the present application;
FIG. 6 is a schematic diagram of enhancement processing in an OSMO optimization improvement method for fault reconstruction, according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a method for extracting an inner target edge neighborhood and an outer target edge neighborhood in an OSMO optimization improvement method for tomographic reconstruction according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of extracting an inner neighboring region of a target edge and an outer neighboring region of the target edge in an OSMO optimization improvement method for tomographic reconstruction according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the technical solutions in this specification, a clear and complete description of the technical solutions in one or more embodiments of this specification will be provided below with reference to the accompanying drawings in one or more embodiments of this specification, and it is apparent that the one or more embodiments described are only a part of embodiments of this specification, not all embodiments. All other embodiments, which can be made by one or more embodiments of the present disclosure without inventive faculty, are intended to be within the scope of the present disclosure.
According to the OSMO optimization improvement method for fault reconstruction, the target edge area is optimized on the basis of an OSMO optimization algorithm, and the printing precision is further improved. The OSMO optimization improvement method for tomographic reconstruction and the respective steps thereof provided by the embodiments of the present specification will be described in detail below.
Example 1
Referring to fig. 1, an improved method for optimizing an OSMO for fault reconstruction according to an embodiment of the present application includes the following steps: s10: and performing OSMO optimization processing on the model structure to obtain an intermediate optimization model and a first intermediate variable of the fault reconstruction target. The model structure is the model structure which needs 3D printing and is mentioned in the background art, and the step is to perform OSMO optimization treatment on the model structure to obtain a final intermediate optimization model of OSMO optimization. S20 is performed on this basis: and carrying out enhancement treatment on the edge area of the intermediate optimization model to obtain a second intermediate variable of the enhancement optimization model and the fault reconstruction target. Thus adding processing steps for target edge enhancement based on OSMO optimization. S30 may be performed after the above two steps are completed: and (3) based on the iterative optimization enhancement optimization model and the second intermediate variable, obtaining an optimal structure of the fault reconstruction target, wherein OSMO is an object space model optimization method.
According to Computed Tomography (CT) and fourier slice theorem, an ideal process for VAM tomographic reconstruction is as shown in fig. 2, where an image set is obtained from a model structure by forward projection calculation, and then a printed structure is obtained by projector projection (called back projection). However, due to light scattering we can only get a low precision printed structure as shown in fig. 3. It is therefore necessary to perform optimization calculations before projection by the projector to obtain a printing structure of higher accuracy, as shown in fig. 4. The optimal structure of the fault reconstruction target is obtained on the basis of an OSMO optimization algorithm, but considering that the most direct evaluation standard of the VAM fault reconstruction precision is the separation degree of a target area and a background area in the optimal structure of the fault reconstruction target, the better the separation of the target area and the background area is, the higher the fault reconstruction precision is; conversely, the lower. According to the embodiment of the application, the edge enhancement processing is added on the basis of the existing OSMO optimization algorithm, so that the fault reconstruction precision and optimization iteration convergence of the target edge area are further improved.
Optionally, in the method for improving the OSMO optimization provided by the embodiment of the present application, performing an OSMO optimization process on a model structure to obtain a first intermediate variable of an intermediate optimization model and a tomographic reconstruction target, including: subtracting a part higher than a first background threshold value from a background area of the model structure to obtain a preliminary intermediate optimization model; adding a first part lower than a target threshold value to a target area of the preliminary intermediate optimization model to obtain a final intermediate optimization model; and obtaining a first intermediate variable based on the final intermediate optimization model.
The step is an OSMO optimization step, a background area of the model structure is processed, and a part higher than a first background threshold value is subtracted to obtain a preliminary intermediate optimization model, wherein the first background threshold value can be a preset experience value, and the value range can be between 0 and 1. And then processing a target area of the preliminary intermediate optimization model to obtain a final intermediate optimization model, and obtaining a first intermediate variable, wherein the first intermediate variable is a printing model after fault reconstruction based on the final intermediate optimization model, and likewise, the second intermediate variable is a printing model after fault reconstruction based on the enhanced optimization model, namely an optimized structure of a fault reconstruction target.
Optionally, in the method for improving the OSMO optimization provided by the embodiment of the present application, adding the first portion lower than the target threshold to the target area of the preliminary intermediate optimization model to obtain the final intermediate optimization model includes: obtaining a preliminary intermediate variable of the fault reconstruction target based on the preliminary intermediate optimization model; a first sub-target threshold portion is obtained based on the preliminary intermediate variable. The preliminary intermediate optimization model is subjected to OSMO optimization once to obtain a final intermediate optimization model, wherein the first part below the target threshold value is obtained by the preliminary intermediate variables.
Optionally, as shown in fig. 6, in the method for improving OSMO optimization provided by the embodiment of the present application, enhancement processing is performed on an edge area of an intermediate optimization model to obtain a second intermediate variable for enhancing the optimization model and a tomographic reconstruction target, where the method specifically includes: adding a second part lower than a target threshold value to the target edge inner area of the final intermediate optimization model to obtain an initial enhancement model; subtracting a second part higher than the background threshold value from the target edge outer area of the initial enhancement model to obtain an enhancement optimization model.
The target edge area of the tomographic reconstruction structure is positioned in the transition area of the target and the background, and the overlapping area of the target and the background occupies a main part in the light distribution histogram statistics of the tomographic reconstruction structure. In the optimization calculation process, the accurate reconstruction of the edge area of the fault reconstruction target is a key for improving the printing precision. Therefore, in the optimization iteration process, the inner adjacent area (In-part edge) of the target edge is enhanced, and the outer adjacent area (Out-of-part edge) of the target edge is weakened, so that the contrast of the target edge area is further improved, and better separation of the target from the background is promoted, as shown In fig. 7 and 8.
The application adds two-step edge enhancement processing based on the existing OSMO algorithm: and firstly, carrying out further enhancement treatment on the adjacent area inside the target edge of the structural model, and secondly, carrying out further weakening treatment on the adjacent area outside the target edge of the structural model so as to further improve the reconstruction accuracy of the target edge part.
Optionally, in the method for improving the OSMO optimization provided by the embodiment of the present application, adding a second portion lower than a target threshold to an inner area of a target edge of a final intermediate optimization model to obtain an initial enhancement model, including: obtaining a second below target threshold portion based on the first intermediate variable; obtaining a secondary intermediate variable based on the initial enhancement model; correspondingly, subtracting the second above background threshold from the target edge outer region of the initial enhancement model to obtain an enhancement optimization model, specifically comprising: obtaining a second above background threshold portion based on the secondary intermediate variable; and obtaining a second intermediate variable based on the enhanced optimization model.
Optionally, in the method for improving the OSMO optimization provided by the embodiment of the present application, the optimal structure of the tomographic reconstruction target is obtained based on the iterative optimization enhancement optimization model of the above steps, which specifically includes: and iteratively optimizing the second intermediate variable based on the enhanced optimization model, wherein the second intermediate variable is the optimal structure of the fault reconstruction target.
The OSMO optimization improvement method provided by the application is to carry out edge enhancement improvement on the basis of the existing OSMO optimization algorithm. Firstly, extracting an In-part edge of a target edge inner adjacent area and an Out-of-part edge of a target edge outer adjacent area of an original model structure by adopting a morphological method. Then, in each optimization iteration process of the OSMO optimization algorithm, enhancement processing of the In-part edge of the adjacent area inside the target edge and weakening processing of the Out-of-part edge of the adjacent area outside the target edge are carried Out.
Examples
The implementation process of the OSMO optimization improvement method (EE OSMO in fig. 5, the full text of EE is Edge Enhanced) provided in this embodiment is described in detail with reference to fig. 5. First, each calculation identifier will be described.
Defining a model structure to be printed as f T . The Reden transform operator (i.e., forward-project) that converts the model structure into an image set is defined as P, and the inverse transform (i.e., backward-project) is defined as P. N represents the normalization operator. D (D) l A light metering normalization threshold value representing a background area, D h A normalized threshold of light metering representing a target area, wherein 0<D l <D h <1。M j,j Representing the model structure after j iterations, namely an initial intermediate optimization model, a final intermediate optimization model and an enhanced optimization model which are mentioned in the foregoing, M 0,0 Is M j,j An initial value of (1), i.e. an initial intermediate optimization model, the value of which is defined as f T . Definition f j,j Is M j,j After the radon transformation and clamping, the radon inverse transformation normalization structure, namely an intermediate variable, comprises a primary intermediate variable, a first intermediate variable, a secondary intermediate variable and a second intermediate variable. Intermediate variable f j,j The expression of (2) is:
(1)
referring to fig. 5, the implementation of the ee OSMO algorithm is as follows:
step 1 from the model structure M i,i The unwanted first above background threshold portion Dl is subtracted from the background region of (c) and updated to the preliminary intermediate optimization model M i,i+1 ：
(2)
Preliminary intermediate variables of tomographic reconstruction structure according to formula (1)The calculation formula of (2) is as follows:
(3)
step 2, optimizing the model M from the preliminary middle i,i+1 Adding the desired first below target threshold portion D h UpdatingOptimizing model M for final intermediate i,i+2 ：
(4)
According to formula (1), a first intermediate variable of the tomographic reconstruction structureThe calculation formula of (2) is as follows:
(5)
step 3, optimizing the model M from the final middle i,i+2 Adding a desired second lower-than-target threshold portion D to the target edge inner vicinity In-part edge h Updated to the initial enhancement model M i,i+3 ：
(7)
Secondary intermediate variables of the tomographic reconstruction structure according to formula (1)The calculation formula of (2) is as follows:
(8)
step 4, enhancing the model M from the initial i,i+3 The unwanted second above background threshold portion D is subtracted from the target edge outside neighborhood Out-of-part edge l Updated to enhance the optimization model M i+1,i+1 ：
(9)
According to formula (1), a second intermediate variableThe calculation formula of (2) is as follows:
(10)
after completing N iterations, the solution of the optimal structure Image Set of the tomographic reconstruction target can be achieved, for example Eq. (6).
(6)
Optionally, as shown in fig. 6, fig. 7, and fig. 8, in the method for improving OSMO optimization provided by the embodiment of the present application, before performing enhancement processing on an edge area of a final intermediate optimization model to obtain an enhanced optimization model, the method further includes: determining edge lines of a target area of the final intermediate optimization model; the inner adjacent area of the target edge is set with the width range within the edge line, and the outer adjacent area of the target edge is set with the width range outside the edge line.
Optionally, as shown in fig. 8, in the method for improving OSMO optimization provided in the embodiment of the present application, a set width range within an edge line is taken as an inner adjacent area of a target edge, and an outer width range outside the edge line is taken as an outer adjacent area of the target edge, which specifically includes: carrying out corrosion treatment on the fault reconstruction target area to obtain a target after corrosion treatment; subtracting the corroded target from the fault reconstruction target area to obtain an adjacent area inside the edge of the target.
Performing corrosion treatment on a Target area Target in the model structure, wherein structural elements for the corrosion treatment are of a central symmetry structure (3×3 structure is selected in the embodiment), so as to obtain a Target Eroded Target after the corrosion treatment as shown in FIG. 7; subtracting the Target area Target from the etched Target Eroded Target to obtain the inner adjacent area of the Target edge, as shown in FIG. 8.
Optionally, as shown in fig. 8, in the method for improving OSMO optimization provided in the embodiment of the present application, a set width range within an edge line is taken as an inner adjacent area of a target edge, and an outer width range outside the edge line is taken as an outer adjacent area of the target edge, which specifically includes: performing expansion treatment on the fault reconstruction target area to obtain an expanded target; subtracting the fault reconstruction target area from the target after the expansion treatment to obtain an external adjacent area of the target edge.
Performing expansion treatment on a Target area Target in the geometric structure, wherein structural elements for the expansion treatment are of a central symmetrical structure (3×3 structure is selected in the application), so as to obtain an expanded Target dialated Target shown in fig. 7; subtracting the Target from the Target area Target after the expansion processing, the Target edge external adjacent area Out-of-part edge of the Target area Target can be obtained, as shown in fig. 8. The size of the structural elements used for edge extraction and erosion in the embodiments may be adjusted according to practical use requirements.
According to the analysis, the OSMO optimization improvement method for fault reconstruction provided by the embodiment of the application is used for performing OSMO optimization treatment on the model structure to obtain a final intermediate optimization model and a first intermediate variable of a fault reconstruction target; performing enhancement treatment on the edge region of the intermediate optimization model to obtain a second intermediate variable of the enhancement optimization model and a fault reconstruction target; and (3) carrying out iterative optimization on the enhanced optimization model and the second intermediate variable based on the steps to obtain an optimal structure of the fault reconstruction target. The most direct evaluation standard of the VAM fault reconstruction precision is the separation degree of a target region and a background region in a fault reconstruction target structure model, and the better the two regions are separated, the higher the fault reconstruction precision is, and the lower the fault reconstruction precision is otherwise. According to the embodiment of the application, the processing steps of edge enhancement are added on the basis of the existing OSMO optimization algorithm, firstly, the processing steps of further enhancing the adjacent area inside the target edge of the model structure and secondly, the processing steps of further weakening the adjacent area outside the target edge of the model structure are carried out, so that the fault reconstruction precision of the target edge area is further improved, and the convergence and accuracy of the iteration of the OSMO optimization algorithm are improved.
Example two
Embodiments of the present application provide a storage medium for computer readable storage, the storage medium storing one or more programs which, when executed by one or more processors, implement an OSMO optimization improvement method for tomographic reconstruction as described above.
Referring to fig. 1, an improved method for optimizing an OSMO for fault reconstruction according to an embodiment of the present application includes the following steps: s10: and performing OSMO optimization processing on the model structure to obtain an intermediate optimization model and a first intermediate variable of the fault reconstruction target. The model structure is the model structure which needs 3D printing and is mentioned in the background art, and the step is to perform OSMO optimization treatment on the model structure to obtain a final intermediate optimization model of OSMO optimization. S20 is performed on this basis: and carrying out enhancement treatment on the edge area of the intermediate optimization model to obtain a second intermediate variable of the enhancement optimization model and the fault reconstruction target. Thus adding processing steps for target edge enhancement based on OSMO optimization. S30 may be performed after the above two steps are completed: and (3) carrying out iterative optimization on the enhanced optimization model and the second intermediate variable based on the steps to obtain an optimal structure of the fault reconstruction target.
According to Computed Tomography (CT) and fourier slice theorem, an ideal process for VAM tomographic reconstruction is as shown in fig. 2, where an image set is obtained from a model structure by forward projection calculation, and then a printed structure is obtained by projector projection (called back projection). However, due to light scattering we can only get a low precision printed structure as shown in fig. 3. It is therefore necessary to perform optimization calculations before projection by the projector to obtain a printing structure of higher accuracy, as shown in fig. 4. The optimal structure of the fault reconstruction target is obtained on the basis of an OSMO optimization algorithm, but considering that the most direct evaluation standard of the VAM fault reconstruction precision is the separation degree of a target area and a background area in the optimal structure of the fault reconstruction target, the better the separation of the target area and the background area is, the higher the fault reconstruction precision is; conversely, the lower. According to the embodiment of the application, the edge enhancement processing is added on the basis of the existing OSMO optimization algorithm, so that the fault reconstruction precision and optimization iteration convergence of the target edge area are further improved.
According to the analysis, the OSMO optimization improvement method for fault reconstruction provided by the embodiment of the application is used for performing OSMO optimization treatment on the model structure to obtain a final intermediate optimization model and a first intermediate variable of a fault reconstruction target; performing enhancement treatment on the edge region of the intermediate optimization model to obtain a second intermediate variable of the enhancement optimization model and a fault reconstruction target; and (3) carrying out iterative optimization on the enhanced optimization model and the second intermediate variable based on the steps to obtain an optimal structure of the fault reconstruction target. The most direct evaluation standard of the VAM fault reconstruction precision is the separation degree of a target region and a background region in a fault reconstruction target structure model, and the better the two regions are separated, the higher the fault reconstruction precision is, and the lower the fault reconstruction precision is otherwise. According to the embodiment of the application, the processing steps of edge enhancement are added on the basis of the existing OSMO optimization algorithm, firstly, the processing steps of further enhancing the adjacent area inside the target edge of the model structure and secondly, the processing steps of further weakening the adjacent area outside the target edge of the model structure are carried out, so that the fault reconstruction precision of the target edge area is further improved, and the convergence and accuracy of the iteration of the OSMO optimization algorithm are improved.
In summary, the foregoing description is only a preferred embodiment of the present application and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the protection scope of the present specification.
The systems, devices, modules, or units illustrated in one or more of the embodiments described above may be implemented in particular by a computer chip or entity, or by a product having some function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
Claims (10)
1. An improved method for optimizing an OSMO for tomographic reconstruction, comprising the steps of:
performing OSMO (object space model) optimization treatment on the model structure to obtain a final intermediate optimization model and a first intermediate variable of a fault reconstruction target, wherein OSMO is an object space model optimization method;
performing enhancement treatment on the edge area of the final intermediate optimization model to obtain an enhancement optimization model and a second intermediate variable of the fault reconstruction target;
and iteratively optimizing the enhanced optimization model and the second intermediate variable based on the steps to obtain the optimal structure of the fault reconstruction target.
2. The method for improving the OSMO optimization of claim 1, wherein performing the OSMO optimization on the model structure to obtain a final intermediate optimization model and a first intermediate variable of the tomographic reconstruction target specifically comprises:
subtracting a first part higher than a background threshold value from a background area of the model structure to obtain a preliminary intermediate optimization model;
adding a first part lower than a target threshold value to a target area of the preliminary intermediate optimization model to obtain a final intermediate optimization model;
and obtaining the first intermediate variable based on the final intermediate optimization model.
3. The OSMO optimization improvement method of claim 2, wherein adding the first sub-target threshold portion to the target region of the preliminary intermediate optimization model results in the final intermediate optimization model, comprising:
obtaining a preliminary intermediate variable of the fault reconstruction target based on the preliminary intermediate optimization model;
the first below target threshold portion is derived based on the preliminary intermediate variable.
4. The OSMO optimization improvement method according to claim 2, wherein the enhancing process is performed on the edge region of the final intermediate optimization model to obtain a second intermediate variable of the enhancement optimization model and the tomographic reconstruction target, specifically including:
adding a second part lower than the target threshold value to the target edge inner area of the final intermediate optimization model to obtain an initial enhancement model;
subtracting a second part higher than the background threshold value from the target edge outer area of the initial enhancement model to obtain an enhancement optimization model.
5. The method of claim 4, wherein adding a second portion below the target threshold to the target edge interior neighborhood of the final intermediate optimization model results in an initial enhancement model, comprising:
obtaining the second below target threshold portion based on the first intermediate variable;
obtaining a secondary intermediate variable based on the initial enhancement model;
correspondingly, subtracting a second part higher than a background threshold value from the adjacent area outside the target edge of the initial enhancement model to obtain an enhancement optimization model, which specifically comprises the following steps:
deriving the second above background threshold portion based on the secondary intermediate variable;
and obtaining a second intermediate variable based on the enhanced optimization model.
6. The OSMO optimization improvement method of claim 5, wherein iteratively optimizing the enhanced optimization model and the second intermediate variable based on the above steps results in an optimal structure of the tomographic reconstruction target, comprising:
iteratively optimizing the second intermediate variable based on the enhanced optimization model;
and the second intermediate variable obtained after n times of optimization iteration is the optimal structure of the fault reconstruction target.
7. The method according to any one of claims 1 to 6, wherein before performing enhancement processing on an edge region of the final intermediate optimization model to obtain an enhanced optimization model, the method further comprises:
determining an edge line of a target area of the final intermediate optimization model;
and taking the inner set width range of the edge line as a target edge inner adjacent area, and taking the outer set width range of the edge line as a target edge outer adjacent area.
8. The OSMO optimization improvement method of claim 7, wherein the setting of the width range within the edge line as the target edge inner vicinity and the setting of the width range outside the edge line as the target edge outer vicinity specifically comprises:
carrying out corrosion treatment on the fault reconstruction target area to obtain a target after corrosion treatment;
subtracting the target after corrosion treatment from the fault reconstruction target area to obtain an adjacent area inside the edge of the target.
9. The OSMO optimization improvement method of claim 8, wherein the setting of the width range within the edge line as the target edge inner vicinity and the setting of the width range outside the edge line as the target edge outer vicinity specifically comprises:
performing expansion treatment on the fault reconstruction target area to obtain an expanded target;
and subtracting the fault reconstruction target area from the target after the expansion treatment to obtain an external adjacent area of the target edge.
10. A storage medium for computer readable storage, the storage medium storing one or more programs which, when executed by one or more processors, implement the OSMO optimization improvement method for tomographic reconstruction according to any one of claims 1 to 9.
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