Document image shadow removing method and device, computer equipment and storage medium

Abstract

The invention discloses a document image shadow removing method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the contrast adjustment of a to-be-processed document shadow image through an image processing technology, and obtaining a contrast heat map of a highlighted shadow region; uniformly dividing the shadow removal process of the document shadow image into K stages representing different shadow scales by adopting a multi-scale shadow flow matching framework, and learning a conditional probability path between data and noise in the current stage through a flow matching model in each stage, performing shadow removal on the current stage according to the conditional probability path; and based on the shadow removal result of the current stage, predicting the shadow scale of the next stage by adopting a next scale prediction mechanism, and guiding the shadow removal of the next stage by utilizing the contrast heat map. According to the method and the device, the dependence on external information is reduced, the shadow can be accurately removed, the detail information in the original document can be reserved, and the shadow removal effect is greatly improved.

Description

Technical Field

[0001] The present application relates to the technical field of document image processing, and in particular to a document image shadow removal method, device, computer equipment and storage medium. Background Art

[0002] With the increasing demand for document digitization, document image processing technology plays an important role in modern information systems. The successful application of tasks such as OCR (Optical Character Recognition), table recognition, and document layout reconstruction has put forward higher requirements on the quality of document images. However, in the actual process of shooting or scanning documents, shadows often appear in document images due to reasons such as light source occlusion and the complexity of ambient light. These shadows will block key information content such as text, table lines, and background details in the document, which will directly lead to a decrease in the accuracy of information extraction and affect the performance of subsequent tasks. Therefore, document shadow removal has become a challenging and important research direction in low-level vision tasks.

[0003] The characteristics of document shadows are different from those of shadows in natural scenes. Natural scene shadow removal usually deals with complex and diverse environmental backgrounds, such as lighting changes, complex textures, or light and shadow interactions between objects. Document images usually have a high information density, especially structured information such as text, tables, and layouts. The presence of shadows not only reduces the readability of documents, but may also lead to information loss or misrecognition. Therefore, document shadow removal requires not only visually removing shadows, but also maintaining the integrity of the document content and the consistency of tones. At present, research on document shadow removal mainly includes the following categories:

[0004] First, the shadow mask-based method: This method generates a shadow mask to guide the removal of shadows, such as using semantic segmentation or specific region marking. However, this type of method relies on high-quality shadow masks, and the generation of the mask itself requires additional annotation or computational overhead.

[0005] 2. Deep learning methods: In recent years, with the development of deep learning technology, some methods have tried to use convolutional neural networks (CNN) or Transformer-based models to improve the removal effect by increasing the network depth and introducing attention mechanisms, but this has also brought about an increase in network design complexity and computational overhead.

[0006] 3. Method based on data enhancement; this method improves the shadow removal effect by constructing a large-scale annotated dataset or frequency domain feature decomposition, such as the SD7K dataset. It requires a large amount of high-quality data and may have insufficient generalization capabilities in different shadow scenarios.

[0007] The inventors found in their research that in document shadows, there is a significant difference in contrast between shadow areas and non-shadow areas. However, among the existing methods, most methods do not fully utilize the information of the document shadow image itself, but rely on external auxiliary tools (such as shadow masks) or complex network designs. The complex network structure and multi-stage training process increase the computing requirements and place too much demand on training resources and reasoning resources. In addition, the existing methods work well when processing specific types of shadows, but are prone to failure in complex and changeable shadow scenes such as uneven lighting or strong projection areas. Summary of the invention

[0008] In view of this, the present application provides a document image shadow removal method, apparatus, computer equipment and storage medium, aiming to solve at least one of the above-mentioned technical problems in the prior art to a certain extent.

[0009] In order to solve the above problems, this application provides the following technical solutions:

[0010] A document image shadow removal method, comprising:

[0011] The image processing technology is used to adjust the contrast of the document shadow image to be processed, and a contrast heat map highlighting the shadow area is obtained;

[0012] A multi-scale shadow flow matching framework is used to evenly divide the shadow removal process of the document shadow image into K stages representing different shadow scales. In each stage, a conditional probability path between data and noise in the current stage is learned through a flow matching model, and shadow removal is performed on the current stage according to the conditional probability path.

[0013] Based on the shadow removal result of the current stage, a next scale prediction mechanism is used to predict the shadow scale of the next stage, and the contrast heat map is used to guide the shadow removal in the next stage;

[0014] When all stages are processed, the final document shadow-free image is output.

[0015] The technical solution adopted by the embodiment of the present application also includes: after using the image processing technology to adjust the contrast of the document shadow image to be processed and obtaining the contrast heat map of the highlighted shadow area, it also includes:

[0016] The contrast heat map is input into an adjustment network, and the contrast heat map is adaptively adjusted by the adjustment network to generate an adaptive shadow scale heat map.

[0017] The technical solution adopted by the embodiment of the present application also includes: before the shadow removal process of the document shadow image is evenly divided into K stages representing different shadow scales using the multi-scale shadow flow matching framework, it also includes:

[0018] The document shadow image is preprocessed; the preprocessing specifically includes: performing multiple downsampling processes on the document shadow image to reduce the resolution of the document shadow image.

[0019] The technical solution adopted by the embodiment of the present application also includes: learning the conditional probability path between data and noise in the current stage through the flow matching model, and removing the shadow of the current stage according to the conditional probability path, specifically:

[0020] The flow matching model is used to interpolate the flow between the data and the compressed low-resolution shadow representation, and the time step of the flow matching model is divided into K resolution windows, each resolution window is half of the previous resolution window; represents the interpolated version, the stream is represented as:

[0021]

[0022] Among them, t represents the time, Down(·,2 k ) represents the downsampling function in the k-th resolution window, is the sampled random noise and x1 is a data sample; for the k-th resolution window [t min,k ,t max,k ], let t′=(tt min,k ) / (t max,k -t min,k ) represents the rescaled time step, then the flow follows:

[0023]

[0024] Among them, Up(·) and Down(·) represent the up-sampling function and the down-sampling function respectively, and Represent the data values ​​at the last and first moments in the kth window respectively.

[0025] The technical solution adopted by the embodiment of the present application also includes: the conditional probability path is defined as follows:

[0026]

[0027] Among them, t max,k and t min,k Respectively represent the last and first moments under the kth window, It means it follows normal distribution;

[0028] At the kth window resolution, the stream is matched to the model v t Regression to the conditional vector field And unify shadow removal and image enhancement through flow matching objectives:

[0029]

[0030] The technical solution adopted by the embodiment of the present application also includes: the calculation formula of the next scale prediction mechanism is:

[0031]

[0032] Where Δt represents the offset time, Δt = t max,t -t min,t , v t Represents a flow matching model.

[0033] The technical solution adopted by the embodiment of the present application also includes: based on the shadow removal result of the current stage, a next scale prediction mechanism is used to predict the shadow scale of the next stage, and the contrast heat map is used to guide the shadow removal in the next stage, specifically:

[0034] The shadow removal result of the current stage is input into the contrast perception module of the next scale prediction mechanism, and the shadow scale of the next stage is adaptively perceived by the contrast perception module, and the shadow scale heat map is used as conditional information to guide the shadow scale prediction of the next stage:

[0035]

[0036] Among them, v θ represents the v-prediction network, c is the extracted contrast-based shadow scale representation, conditioned on the flow matching model via cross-attention, and x inp is the document shadow image, is the target image predicted at time t, It is the shadow scale prediction result of the previous stage.

[0037] Another technical solution adopted by the embodiment of the present application is: a document image shadow removal device, comprising:

[0038] Contrast adjustment module: used to adjust the contrast of the document shadow image to be processed by using image processing technology, and obtain a contrast heat map highlighting the shadow area;

[0039] Flow matching module: used for evenly dividing the shadow removal process of the document shadow image into K stages representing different shadow scales by adopting a multi-scale shadow flow matching framework. In each stage, the conditional probability path between the data and the noise in the current stage is learned by the flow matching model, and the shadow removal is performed on the current stage according to the conditional probability path.

[0040] Scale prediction module: used to predict the shadow scale of the next stage based on the shadow removal result of the current stage by using the next scale prediction mechanism, use the contrast heat map to guide the shadow removal of the next stage, and output the final document shadow-free image after all stages are processed.

[0041] Compared with the prior art, the beneficial effects produced by the embodiments of the present application are: the document image shadow removal method, device, computer equipment and storage medium of the embodiments of the present application effectively locate the shadow area by utilizing the contrast information in the document image, and use it as the core condition for shadow removal. By designing a multi-scale shadow flow matching framework, the shadow removal process of the document shadow image is divided into multiple stages representing different shadow scales, and the document shadow image is shadowed in stages according to the shadow scale. In the shadow removal process, the contrast information is used as conditional information to achieve efficient and automatic shadow detection and positioning, which greatly reduces computational redundancy. The embodiments of the present application reduce the dependence on external information, do not require additional annotations or external auxiliary data, and can accurately remove shadows and retain the detail information in the original document in complex and changeable shadow scenes such as uneven lighting or strong projection areas, greatly improving the shadow removal effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of a document image shadow removal method according to an embodiment of the present application;

[0043] Figure 2 is a schematic diagram of image contrast correlation in an embodiment of the present application, wherein (a) is a document shadow image, and (b) is a contrast heat map extracted from the document shadow image;

[0044] Figure 3 It is a shadow scale heat map adjusted by adjusting the network in the embodiment of the present application;

[0045] Figure 4 A schematic diagram of a multi-scale shadow flow matching framework according to an embodiment of the present application;

[0046] Figure 5 This is a schematic diagram of the structure of a document image shadow removal device according to an embodiment of the present application;

[0047] Figure 6 A schematic diagram of the computer device structure of an embodiment of the present application;

[0048] Figure 7 A schematic diagram of the structure of a storage medium according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] The terms "first", "second", "third" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second", "third" can expressly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. In the embodiments of this application, all directional indications (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or computer device that includes a series of steps or units is not limited to the steps or units listed, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or computer devices.

[0051] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0052] Specifically, see Figure 1 , is a flow chart of a document image shadow removal method according to an embodiment of the present application. The document image shadow removal method according to an embodiment of the present application comprises the following steps:

[0053] S100: Acquire a document shadow image to be processed;

[0054] S110: adjusting the contrast of the document shadow image by using image processing technology to generate a contrast heat map highlighting the shadow area;

[0055] In this step, since there is a significant difference in contrast between the shadow area and the non-shadow area in the document shadow image, in order to more reasonably represent the shadow scale, the embodiment of the present application starts with contrast analysis, and uses the significant difference in contrast between the shadow area and the non-shadow area to adjust the contrast of the document shadow image through image processing technology to generate a contrast heat map c with a clear contrast. h , which can not only effectively locate the shadow area, but also provide rich guidance information for the shadow removal process without the need for additional annotation or external auxiliary data. Figure 2 , which is a schematic diagram of image contrast in an embodiment of the present application, wherein (a) is a document shadow image, and (b) is a contrast heat map extracted from the document shadow image. In the embodiment of the present application, the image processing technology includes but is not limited to image digitization or image enhancement and restoration.

[0056] S120: inputting the contrast heat map into the adjustment network, adaptively adjusting the contrast heat map through the adjustment network, and generating an adaptive shadow-scale heat map (Shadow-Scale Heatmap);

[0057] In this step, since different images have different responses to contrast adjustment, the embodiment of the present application uses the contrast heat map c generated by the image processing technology to generate the contrast heat map c. h Input into the adjustment network for adaptive adjustment to adapt to images under different lighting conditions and generate an adaptive shadow scale heat map c = a θ (c h ). Figure 3 As shown, the shadow scale heat map after adjusting the network in the embodiment of the present application is shown. The shadow scale heat map can adapt to the contrast differences of different images, capture the intensity and position characteristics of the shadow area, and can be used as conditional information to guide shadow removal. Compared with static masks, the adaptive dynamic adjustment method is more adaptable and accurate.

[0058] S130: preprocessing the document shadow image, and using a multi-shadow-scale flow matching framework to evenly divide the shadow removal process of the document shadow image into K stages representing different shadow scales;

[0059] In this step, the preprocessing includes: performing multiple downsampling processes on the document shadow image to reduce the resolution of the document shadow image and reduce the amount of calculation for subsequent processing; then, dividing the shadow removal process of the document shadow image into multiple stages (Stage k, Stage k+1, ...), so that each stage corresponds to a different shadow scale.

[0060] S140: In each stage, a conditional probability path between data and noise in the current stage is learned through a flow matching model, and shadow removal is performed on the document shadow image in the current stage according to the conditional probability path;

[0061] In this step, flow matching is similar to the diffusion model, which aims to learn a velocity field v t , random noise is converted into Mapping to data samples x1~q:

[0062]

[0063] The flow matching framework directly regresses the velocity v t To the conditional vector field u t (·|x1), which provides a simplified training objective for the flow generation model without simulation:

[0064]

[0065] Among them, u t (·|x1) uniquely determines a conditional probability path leading to data sample x1. An effective choice of conditional probability path is the linear interpolation of data and noise:

[0066]

[0067] And u(x t |x1)=x1-x0.

[0068] Flow matching can be flexibly extended to interpolation between distributions other than the standard Gaussian distribution.

[0069] Furthermore, under the multi-scale shadow flow matching framework, the document shadow image in the early stage exhibits strong shadows and severe degradation. Therefore, the early stage only needs to focus on global illumination adjustment and removal of severe shadows at a lower resolution to reduce computational costs. As shadow removal progresses, the shadow of the image gradually becomes lighter. The mid-term stage requires local area restoration at a medium resolution, while the late stage requires fine detail enhancement and text restoration at a high resolution. To reduce redundant calculations in the early stages, an embodiment of the present application interpolates a flow between data and a compressed low-resolution shadow representation through a flow matching model. Specifically, the time step of the flow matching model is divided into K resolution windows, each resolution window is half of the previous resolution window; represents the interpolated version, the stream can be represented as:

[0070]

[0071] Where t represents time, Down(·,2 k ) represents the downsampling function in the k-th resolution window, is the sampled random noise and x1 is a data sample. For the k-th resolution window [t min,k ,t max,k ], let t′=(tt min,k ) / (t max,k -t min,k ) represents the rescaled time step, then its flow follows:

[0072]

[0073] Among them, Up(·) and Down(·) represent the up-sampling function and the down-sampling function respectively. and Represent the data values ​​at the last and first moments in the kth window, respectively. In this way, only the last stage performs shadow removal at full resolution, while most other stages complete shadow removal at low resolution. Under uniform stage division, the multi-scale shadow flow matching framework reduces the computation time to about 1 / K, thereby reducing the computational requirements.

[0074] Furthermore, if Figure 4 As shown, it is a schematic diagram of the multi-scale shadow flow matching framework of an embodiment of the present application. In the process of constructing the multi-scale shadow flow matching framework, the main goal is to uniformly model the different stages. In order to unify the goals of shadow removal and image enhancement, the embodiment of the present application constructs a conditional probability path by interpolating between different resolutions and shadow level conditions, starting from a low-resolution high-noise, high-shadow version, and gradually generating clearer and more detailed results in high resolution. Specifically, the conditional probability path is defined as follows:

[0075]

[0076]

[0077] In the above formula, t max,k and t min,k Respectively represent the last and first moments under the kth window, It means that it obeys the normal distribution, the former is the mean and the latter is the variance.

[0078] Next, at the kth window resolution, the stream is matched to the model v t Regression to the conditional vector field And the shadow removal and image enhancement are unified through the following stream matching targets:

[0079]

[0080] S150: Based on the shadow removal result of the current stage, the shadow scale of the next stage is predicted through the next scale prediction mechanism, and the shadow scale heat map is used as conditional information to guide the shadow removal of the next stage;

[0081] In this step, the next shadow-scale prediction mechanism is constructed by combining the generation ability of the diffusion model and the efficiency of the multi-scale framework. The shadow removal tasks of different intensities are distributed to different stages. The shadow removal results of the current stage are used to gradually predict the shadow scale of the next stage, and the shadow scale heat map is used as conditional information to guide the shadow removal of the next stage, thereby gradually generating high-quality document shadow-free images.

[0082] Specifically, the calculation formula of the next scale prediction mechanism is:

[0083]

[0084] Where Δt represents the offset time, Δt = t max,t -t min,t , v t Represents a flow matching model.

[0085] This process combines the iterative generation and enhancement capabilities of the diffusion model with the next-scale prediction advantages of the autoregressive model to form an efficient training and inference framework, which improves the shadow removal effect while reducing the amount of computation.

[0086] By using the shadow removal result of the current stage as the quantization of the shadow scale of each stage, it is input into the contrast perception module of the next scale prediction mechanism. The shadow scale of the next stage is adaptively perceived by the contrast perception module, and the shadow scale heat map is used as conditional information to guide the shadow scale prediction of the next stage:

[0087]

[0088]

[0089]

[0090] Among them, v θ represents the v-prediction network, c is the extracted contrast-based shadow scale representation, conditioned on the flow matching model via cross-attention, and x inp is the input document shadow image, is the target image predicted at time t. is the shadow scale prediction result of the previous stage, which is used as the conditional information of the current stage. Therefore, at t = 0, xinp As x inp and Then Connect on the channel.

[0091] S160: When all stages are processed, the final document shadow-free image is output.

[0092] Based on the above, the document image shadow removal method of the embodiment of the present application effectively locates the shadow area by utilizing the contrast information in the document image, and uses it as the core condition for shadow removal. By designing a multi-scale shadow flow matching framework, the shadow removal process of the document shadow image is divided into multiple stages representing different shadow scales, and the document shadow image is shadow removed in stages according to the shadow scale. In the shadow removal process, the contrast information is used as conditional information to achieve efficient and automatic shadow detection and positioning, which greatly reduces computational redundancy. The embodiment of the present application reduces the dependence on external information, does not require additional annotations or external auxiliary data, and can accurately remove shadows and retain detailed information in the original document in complex and changeable shadow scenes such as uneven lighting or strong projection areas, greatly improving the shadow removal effect.

[0093] See also Figure 5 , is a schematic diagram of the structure of a document image shadow removal device according to an embodiment of the present application. The document image shadow removal device 40 according to an embodiment of the present application comprises:

[0094] Contrast adjustment module 41: used to adjust the contrast of the document shadow image to be processed by using image processing technology, and obtain a contrast heat map highlighting the shadow area;

[0095] Flow matching module 42: used for evenly dividing the shadow removal process of the document shadow image into K stages representing different shadow scales by adopting a multi-scale shadow flow matching framework, and in each stage, learning a conditional probability path between data and noise in the current stage by a flow matching model, and performing shadow removal on the current stage according to the conditional probability path;

[0096] Scale prediction module 43: used to predict the shadow scale of the next stage based on the shadow removal result of the current stage by using the next scale prediction mechanism, use the contrast heat map to guide the shadow removal of the next stage, and output the final document shadow-free image after all stages are processed.

[0097] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0098] The device provided in the embodiment of the present application can be applied in the aforementioned method embodiment. For details, please refer to the description of the aforementioned method embodiment, which will not be repeated here.

[0099] See also Figure 6 , is a schematic diagram of the computer device structure of an embodiment of the present application. The computer device 50 includes:

[0100] A memory 51 storing executable program instructions;

[0101] A processor 52 connected to the memory 51;

[0102] The processor 52 is used to call the executable program instructions stored in the memory 51 and perform the following steps: using image processing technology to adjust the contrast of the document shadow image to be processed, and obtaining a contrast heat map of the highlighted shadow area; using a multi-scale shadow flow matching framework to evenly divide the shadow removal process of the document shadow image into K stages representing different shadow scales, in each stage, using the flow matching model to learn the conditional probability path between the data and noise in the current stage, and performing shadow removal on the current stage according to the conditional probability path; based on the shadow removal result of the current stage, using the next scale prediction mechanism to predict the shadow scale of the next stage, and using the contrast heat map to guide the shadow removal of the next stage; when all stages are processed, output the final document shadow-free image.

[0103] The processor 52 may also be referred to as a CPU (Central Processing Unit). The processor 52 may be an integrated circuit chip having signal processing capabilities. The processor 52 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0104] See also Figure 7, which is a schematic diagram of the structure of the storage medium of the embodiment of the present application. The storage medium of the embodiment of the present application stores program instructions 61 that can implement the following steps: using image processing technology to adjust the contrast of the document shadow image to be processed, and obtaining a contrast heat map of the highlighted shadow area; using a multi-scale shadow flow matching framework to evenly divide the shadow removal process of the document shadow image into K stages representing different shadow scales, in each stage, using the flow matching model to learn the conditional probability path between the data and noise in the current stage, and removing the shadow of the current stage according to the conditional probability path; based on the shadow removal result of the current stage, using the next scale prediction mechanism to predict the shadow scale of the next stage, and using the contrast heat map to guide the next stage to remove the shadow; when all stages are processed, output the final document shadow-free image.

[0105] Among them, the program instruction 61 can be stored in the above-mentioned storage medium in the form of a software product, including several instructions for a computer device (which can be a personal computer, a server, or a network computer device, etc.) or a processor (processor) to execute all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program instructions, or terminal computer devices such as computers, servers, mobile phones, tablets, etc. Among them, the server can be an independent server, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0106] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0107] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the description and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A document image shadow removal method, characterized in that: include: The image processing technology is used to adjust the contrast of the document shadow image to be processed, and a contrast heat map highlighting the shadow area is obtained; A multi-scale shadow flow matching framework is used to evenly divide the shadow removal process of the document shadow image into K stages representing different shadow scales. In each stage, a conditional probability path between data and noise in the current stage is learned through a flow matching model, and shadow removal is performed on the current stage according to the conditional probability path. Based on the shadow removal result of the current stage, a next scale prediction mechanism is used to predict the shadow scale of the next stage, and the contrast heat map is used to guide the shadow removal in the next stage; When all stages are processed, the final document shadow-free image is output.

2. The document image shadow removal method according to claim 1, characterized in that: After using the image processing technology to adjust the contrast of the document shadow image to be processed and obtaining a contrast heat map highlighting the shadow area, the method further includes: The contrast heat map is input into an adjustment network, and the contrast heat map is adaptively adjusted by the adjustment network to generate an adaptive shadow scale heat map.

3. The document image shadow removal method according to claim 2, characterized in that: Before the shadow removal process of the document shadow image is uniformly divided into K stages representing different shadow scales using the multi-scale shadow flow matching framework, the method further includes: The document shadow image is preprocessed; the preprocessing specifically includes: performing multiple downsampling processes on the document shadow image to reduce the resolution of the document shadow image.

4. The document image shadow removal method according to claim 3, characterized in that: The conditional probability path between data and noise in the current stage is learned through the flow matching model, and shadow removal is performed on the current stage according to the conditional probability path, specifically: The flow matching model is used to interpolate the flow between the data and the compressed low-resolution shadow representation, and the time step of the flow matching model is divided into K resolution windows, each resolution window is half of the previous resolution window; represents the interpolated version, the stream is represented as: Where t represents time, Down(·,2 k ) represents the downsampling function in the k-th resolution window, is the sampled random noise and x1 is a data sample; for the Kth resolution window [t min,k ,t max,k ], let t′=(tt min,k ) / (t max,k -t min,k ) represents the rescaled time step, then the flow follows: Among them, Up(·) and Down(·) represent the up-sampling function and the down-sampling function respectively. and Represent the data values ​​at the last and first moments in the kth window respectively.

5. The document image shadow removal method according to claim 4, characterized in that: The conditional probability path is defined as follows: Among them, t max,k and t min,k Respectively represent the last and first moments under the kth window, It means it follows normal distribution; At the kth window resolution, the stream is matched to the model v t Regression to the conditional vector field And unify shadow removal and image enhancement through flow matching objectives:

6. The document image shadow removal method according to any one of claims 1 to 5, characterized in that: The calculation formula of the next scale prediction mechanism is: Where Δt represents the offset time, Δt = t max,t -t min,t , v t Represents a flow matching model.

7. The document image shadow removal method according to claim 6, characterized in that: Based on the shadow removal result of the current stage, the shadow scale of the next stage is predicted by the next scale prediction mechanism, and the contrast heat map is used to guide the shadow removal in the next stage, specifically: The shadow removal result of the current stage is input into the contrast perception module of the next scale prediction mechanism, and the shadow scale of the next stage is adaptively perceived by the contrast perception module, and the shadow scale heat map is used as conditional information to guide the shadow scale prediction of the next stage: Among them, v θ represents the v-prediction network, c is the extracted contrast-based shadow scale representation, conditioned on the flow matching model via cross-attention, and x inp is the document shadow image, is the target image predicted at time t, It is the shadow scale prediction result of the previous stage.

8. A document image shadow removal device, characterized in that: include: Contrast adjustment module: used to adjust the contrast of the document shadow image to be processed by using image processing technology, and obtain a contrast heat map highlighting the shadow area; Flow matching module: used for evenly dividing the shadow removal process of the document shadow image into K stages representing different shadow scales by adopting a multi-scale shadow flow matching framework. In each stage, the conditional probability path between the data and the noise in the current stage is learned by the flow matching model, and the shadow removal is performed on the current stage according to the conditional probability path. Scale prediction module: used to predict the shadow scale of the next stage based on the shadow removal result of the current stage by using the next scale prediction mechanism, use the contrast heat map to guide the shadow removal of the next stage, and output the final document shadow-free image after all stages are processed.

9. A computer device, characterized in that: The computer device includes a processor and a memory coupled to the processor, wherein: The memory stores program instructions for implementing the document image shadow removal method according to any one of claims 1 to 7; The processor is used to execute the program instructions stored in the memory to control the robot to perform a document image shadow removal method.

10. A storage medium, characterized in that: The invention stores program instructions executable by a processor, wherein the program instructions are used to execute the document image shadow removal method according to any one of claims 1 to 7.

Images