Controllable recovery method, device and equipment for low-sampling frequency spectrum situation map and medium

Abstract

The invention discloses a low-sampling frequency spectrum situation map controllable recovery method, device and equipment and a medium, and belongs to a low-sampling image recovery technology. The method comprises the following steps: acquiring a low-sampling image; recovering the low-sampling image by using different interpolation methods to obtain images with different degrees of interpolation adaptation; filtering and finding out a satisfactory image from the adapted images, and converting the found image into a condition image of a graph generation image; and inputting the condition image into a diffusion model based on particle filtering for processing, and outputting a final recovered image. According to the method, the capability of an existing text and image-to-image diffusion model is enhanced through interpolation adaptation, the weak priori of a two-dimensional image is enhanced, the overall performance is improved, and the diffusion model can effectively process complex scenes related to weak priori appearances. According to the invention, a diffusion model adaptation method combined with particle filtering is integrated into a pre-trained image-to-image diffusion model, so that the recovery quality is further improved.

Description

Technical Field

[0001] The present invention relates to a low-sampling image restoration technology, and in particular to a controllable restoration method, device, equipment and medium for a low-sampling spectrum situation map. Background Art

[0002] With the rapid development of generative techniques, researchers have begun to pay more and more attention to diffusion models on visual generation tasks, especially the great progress in text-to-image (T2I), but generating high-quality visual content based on text or images is still a challenging task, which requires not only generating visually realistic and smooth appearance based on given text or images, but also considering diversity and controllability at the same time. The early development of image diffusion models focused on a single guide, either through images or text. Stable diffusion models use enhanced training data to enhance the ability of diffusion models, thereby improving generation results. ControlNet, GLIGEN, and IP-Adapter extend these methods by integrating multiple types of conditional guides, such as text, images, edge maps, depth maps, etc., providing greater flexibility in image generation. However, despite the versatility of these methods, they mainly tend to rely on text or other conditions to generate diverse image appearances, and it is difficult to produce high-quality results when the image appearance is undersampled. For example, if the sampling rate is "30%~50%", the corresponding conditional image may produce satisfactory results. However, when the sampling rate is changed to "5%", the model often fails to provide high-quality restoration quality. This weak prior on the conditional image poses additional challenges to the diffusion process.

[0003] In addition, some researchers have found great potential based on deep learning techniques. For example, the Unet-based Generative Adversarial Network (GAN) image generator, however, this approach often encounters challenges related to mode collapse, which greatly limits their effectiveness. Secondly, the generation technology based on autoregressive model learning data distribution. Although this can improve data quality and stable model convergence, it requires considerable computing resources. Summary of the invention

[0004] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the object of the present invention is to provide a method, device, equipment and medium for controllable recovery of a low-sampling spectrum situation map based on a diffusion model.

[0005] The first technical solution adopted by the present invention is:

[0006] A controllable recovery method for a low-sampling spectrum situation map comprises the following steps:

[0007] Get a downsampled image;

[0008] Different interpolation methods are used to restore low-sampled images to enhance the weak prior of appearance in the image generation process and obtain images with different degrees of interpolation adaptation;

[0009] Filter the adapted images to find a more satisfactory image, and convert the found image into an image-to-image (I2I) conditional image;

[0010] The conditional image is input into the diffusion model based on particle filtering for processing, and the final restored image is output.

[0011] Furthermore, the step of inputting the conditional image into a diffusion model based on particle filtering for processing and outputting a final restored image comprises:

[0012] Set the time step B and particle size S. In general diffusion models, S defaults to 1, which means that the average sample is taken as the final result. If S is large enough, the final error becomes smaller.

[0013] When the diffusion model is reversed, at each time step t changes from N to 1, an average sample is generated from the distribution Among them, i∈[1,S];

[0014] Through the particle filtering method, from the sample Randomly select S middle samples as replacement samples;

[0015] These S replacement samples are represented as

[0016] From S samples with replacement The final satisfactory image χ0 is obtained.

[0017] Furthermore, the particle filtering method works as follows:

[0018] Initialization and state prediction of particles: The initial particles represent the initial hypothetical state of the data; in the image restoration task, particles can represent the initial low-quality or noisy image; each particle has its weight, and the initial weights are usually equal; according to the state transition model, the state of the particles is predicted (noise is added), that is, the particles are allowed to evolve in the next diffusion process;

[0019] The forward process of the diffusion model: Similar to the standard diffusion model, noise is gradually added to the hypothetical state of the particle, so that the state gradually diffuses to a completely random state;

[0020] Particle weight update: In each diffusion step, the particle weight is updated based on the observation (such as low resolution or partial data) and the current particle state; the weight update reflects the consistency between the particle state and the true observation value; the particle with a higher weight indicates that its state is more likely to be close to the true target data;

[0021] Resampling and replacement: When the weights of some particles are low, particles are regenerated through the resampling mechanism;

[0022] Reverse diffusion process: In the reverse process of the diffusion model, particles gradually remove noise based on the observed data; at this time, the particle filter controls the denoising process through the state estimation of the particles, so that the final state is closer to the real target data.

[0023] Furthermore, different interpolation methods include nearest neighbor interpolation method (NN), ordinary Kriging interpolation method (OK) or radial basis function interpolation method (RBF).

[0024] Further, selecting a suitable interpolation method: according to the specific characteristics and data distribution of the low-sampled image, one or more interpolation methods are selected for restoration;

[0025] Fusion of multiple interpolation methods: Combine multiple interpolation methods to generate multiple restored versions of the image, thereby improving the diversity of restoration; among them, each interpolation method can adapt to different areas to improve the overall effect.

[0026] Further, the selecting one or more interpolation methods for restoration includes:

[0027] For simple scenes, select the nearest neighbor interpolation method for restoration; for complex or random scenes, select the Kriging interpolation method or the radial basis function interpolation method for restoration.

[0028] Furthermore, filtering and finding a more satisfactory image from the adapted images includes:

[0029] Index screening criteria: Screening the images generated by interpolation according to the predetermined index evaluation criteria;

[0030] Automatic scoring mechanism: Automatically score multiple interpolated images and select the image that is closest to the real scene;

[0031] Final screening: Based on the index screening criteria or automatic scoring results, one or more images are selected as the initial conditions for subsequent processing. These images will be used as the conditional input of the image generation image to ensure that the final restored image has a high quality.

[0032] The second technical solution adopted by the present invention is:

[0033] A controllable recovery device for a low-sampling spectrum situation map, comprising:

[0034] Image acquisition module, acquiring low-sampled images;

[0035] The interpolation adaptation module uses different interpolation methods to restore the low-sampled image to enhance the weak prior of the appearance in the image generation process and obtain images with different degrees of interpolation adaptation;

[0036] The image filtering module filters the adapted image to find a more satisfactory image and converts the found image into an image-to-image (I2I) conditional image;

[0037] The image restoration module inputs the conditional image into the diffusion model based on particle filtering for processing and outputs the final restored image.

[0038] The third technical solution adopted by the present invention is:

[0039] An electronic device comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement a controllable recovery method of a low-sampling spectrum situation map as described above.

[0040] The fourth technical solution adopted by the present invention is:

[0041] A computer-readable storage medium, wherein at least one instruction, at least one program, code set or instruction set is stored in the storage medium, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement a controllable recovery method of a low-sampling spectrum situation map as described above.

[0042] The fifth technical solution adopted by the present invention is:

[0043] A computer program product or a computer program, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above-mentioned controllable recovery method of a low-sampling spectrum situation map.

[0044] The beneficial effects of the present invention are as follows: the present invention enhances the capabilities of existing text and image-to-image diffusion models through interpolation adaptation, enhances the weak prior of two-dimensional images, not only improves the overall performance, but also enables the diffusion model to effectively handle complex scenes involving weak prior appearances. The present invention proposes a diffusion model adaptation method combined with particle filtering integrated into a pre-trained image-to-image diffusion model to further improve the restoration quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 This is a schematic diagram of the results of the image restoration method of the ControlNet diffusion model;

[0047] Figure 2 It is a schematic diagram of a controllable recovery algorithm of a low-sampling spectrum situation diagram based on a diffusion model in an embodiment of the present invention;

[0048] Figure 3 This is the interpolation adaptation phase process in an embodiment of the present invention;

[0049] Figure 4 This is the diffusion model adaptation phase process in an embodiment of the present invention;

[0050] Figure 5 It is a flow chart of the steps of a controllable recovery method of a low-sampling spectrum situation diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0052] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0053] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0054] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0055] In order to solve the existing technical problems, the present invention proposes a technical solution to generate high-quality and strong consistency images through interpolation adaptation and particle filtering. In short, the goal of the present invention is to enhance the weak prior brought by text or image by incorporating an additional interpolation adaptation. The interpolation adaptation ensures better appearance details of the image before inputting it into the diffusion model. In addition, in order to effectively improve the image restoration quality, the diffusion model is adapted in combination with the particle filtering method so that the diffusion model obtains accurate posterior sampling.

[0056] Diffusion models have achieved great success in high-dimensional data such as image generation, which provides ideas for this. These models provide strong data priors. Specifically, the stable diffusion model has achieved good results by optimizing the training dataset, greatly improving the efficiency of image restoration. The rapid progress of generation technology has promoted significant progress and continuous innovation in various fields of image generation, such as text-to-image generation (T2I), image-to-image generation (I2I), and ControlNet, which increases the diversity of image generation by using additional conditional information, such as depth, pose, trajectory, and frequency. Figure 1 As shown, the edge map is used as the conditional image, but these techniques are all in the direction of diverse image-to-image generation (see Figure 1 The first row), while the present invention mainly prefers the controllable generation of low-sampled images without text to images (see Figure 1 The generated text is "a blue bird" for the first row of images, and "Restore original image" for the second row of images.

[0057] Diffusion Posterior Sampling (DPS) provides a new approach to this problem, extending the diffusion solver to effectively handle general noisy (non)linear inverse problems through the approximation of posterior sampling. Interestingly, the resulting posterior sampling scheme is a hybrid version of diffusion sampling with manifold-constrained gradients, without a strict measurement consistency projection step, and produces more ideal generation paths in noisy environments compared to previous studies.

[0058] The goal of the present invention is to use the latest T2I diffusion model to restore the original image with high quality, and use interpolation adaptation to alleviate the limitations of the T2I model in the weak prior appearance of the image in complex scenes, and improve the visual accuracy of image restoration. Secondly, the present invention uses the diffusion model adaptation method, which is a clever technique that can skillfully make the solution space of the diffusion model for image restoration more accurate, which can not only improve the overall performance, but also enable the image diffusion model to skillfully locate complex scenes involving weak prior appearance. The interpolation-adapted image is used to guide the T2I model, which does not require additional training or fine-tuning, and optimizes the overall processing speed.

[0059] Example 1

[0060] like Figure 5 As shown, this embodiment provides a controllable recovery method for a low-sampling spectrum situation map, comprising the following steps:

[0061] S1. Obtain a low-sampled image.

[0062] First, collect low-sampled images as source images.

[0063] S2. Use different interpolation methods to restore the low-sampled image to enhance the weak prior of the appearance in the image generation process and obtain images with different degrees of interpolation adaptation.

[0064] An auxiliary interpolation adaptation method is used to enhance the weak prior of the appearance in the whole complex image generation process, and obtain interpolation adaptation images of different degrees. Different interpolation methods include nearest neighbor interpolation method (NN), ordinary Kriging interpolation method (OK) or radial basis function interpolation method (RBF).

[0065] Exemplarily, step S2 specifically includes the following steps:

[0066] S21. Select a suitable interpolation method: Select one or more interpolation methods for restoration according to the specific characteristics and data distribution of the low-sampled image. Specifically, for simple scenes, the nearest neighbor interpolation method is selected for restoration; for complex or random scenes, the Kriging interpolation method or the radial basis function interpolation method is selected for restoration.

[0067] S22. Fusion of multiple interpolation methods: Combine multiple interpolation methods to generate multiple restored versions of the image, thereby improving the diversity of restoration; wherein each interpolation method can adapt to different areas to improve the overall effect.

[0068] S3. Filter and find a more satisfactory image from the adapted image, and convert the found image into a conditional image of the image-generated image.

[0069] A more satisfactory image is filtered out from the interpolation adaptation method and converted into an image-to-image (I2I) conditional image. As an optional implementation, step S3 specifically includes the following steps:

[0070] S31, index screening criteria: screening the images generated by interpolation according to the predetermined index evaluation criteria;

[0071] S32, automatic scoring mechanism: automatically scoring multiple interpolated images and selecting the image closest to the real scene;

[0072] S33, Final Screening: According to the index screening criteria or automatic scoring results, one or more images are selected as the initial conditions for subsequent processing. These images will be used as the conditional input of the image generation image to ensure that the final restored image has a high quality.

[0073] S4. Input the conditional image into the diffusion model based on particle filtering for processing, and output the final restored image.

[0074] The diffusion model with particle filtering is used to better maintain the strong consistency of I2I image generation, and finally a more satisfactory image is restored.

[0075] In some embodiments, step S4 specifically includes:

[0076] S41, set the time step N, particle size S, the default value of S in the general diffusion model is 1, which means that the average sample is regarded as the final result. If S is large enough, the final error becomes smaller;

[0077] S42. When the diffusion model is reversed, as the time step t changes from N to 1, an average sample is generated from the distribution

[0078] S43, through the particle filtering method, from the sample Randomly select S middle samples as replacement samples;

[0079] S44, these S replacement samples are expressed as

[0080] S45, replace samples from S The final satisfactory image χ0 is obtained.

[0081] As an optional implementation, step S43 specifically includes the following steps:

[0082] S431, particle initialization and state prediction: The initial particle represents the initial hypothetical state of the data; in the image restoration task, the particle can represent the initial low-quality or noisy image; each particle has its weight, and the initial weights are usually equal; according to the state transition model, the particle state is predicted (noise is added), that is, the particle is allowed to evolve in the next diffusion process;

[0083] S432, forward process of diffusion model: similar to the standard diffusion model, noise is gradually added to the hypothetical state of the particle, so that the state gradually diffuses to a completely random state;

[0084] S433, particle weight update: In each diffusion step, based on the observation (such as low resolution or partial data) and the current state of the particle, the particle weight is updated; the weight update reflects the consistency between the particle state and the true observation value; the particle with a higher weight indicates that its state is more likely to be close to the true target data;

[0085] S434, resampling and replacement: when the weights of some particles are low, the particles are regenerated through the resampling mechanism;

[0086] S435, reverse diffusion process: In the reverse process of the diffusion model, particles gradually remove noise according to the observed data; at this time, the particle filter controls the denoising process through the state estimation of the particles, so that the final state is closer to the real target data.

[0087] The method of this embodiment not only improves the overall performance, but also enables the diffusion model to effectively handle complex scenes involving weak prior appearance without the need for additional training or fine-tuning.

[0088] The above method is explained in detail below with reference to the accompanying drawings and specific embodiments.

[0089] See also Figure 2 , Figure 2 A controllable restoration method for 2D low-sampling spectrum situation map based on diffusion model is demonstrated. The method specifically includes interpolation adaptation stage and diffusion model adaptation stage.

[0090] (1) Interpolation and adaptation stage

[0091] The goal of this process is to interpolate the input image. In this embodiment, we mainly consider the interpolation quality and interpolation time to obtain the expected image. Finally, we choose the most satisfactory method as our interpolation adaptation method. This stage specifically includes the following steps:

[0092] Step 1: Collect downsampled images.

[0093] Step 2: Get the adapted image. First, restore the collected image using different interpolation methods to get the adapted image, such as Figure 3 shown.

[0094] Step 3: Select the adapted images interpolated by different methods for filtering. Consider the random uncertainty and interpolation effect brought by different interpolation methods, and finally obtain the most satisfactory image.

[0095] (2) Diffusion model adaptation stage

[0096] Particle filter-based methods use a customized proposal distribution to obtain more accurate samples. Figure 4 As shown, this stage specifically includes the following steps:

[0097] Step 1: Input image y0, set time step N, particle size S;

[0098] Step 2: Generate samples q(y 1:N |y0) in {y k} sequence, k∈[1,N];

[0099] Step 3: According to the sequence {y k Generate sample

[0100] Step 4: Standard diffusion model, with each step where k changes from N to 1, a sample is generated from the distribution Through the particle filtering method, the samples Randomly select S replacement samples from ; denote these S replacement samples as

[0101] Step 5: From S new samples The final image χ0 is obtained from .

[0102] In summary, the method of the present invention has the following advantages and beneficial effects compared with the prior art solutions:

[0103] (1) The present invention enhances the capabilities of existing text and image-to-image diffusion models through interpolation adaptation. It first applies an auxiliary interpolation method, the specific task of which is to enhance the weak prior of the two-dimensional image.

[0104] (2) The present invention proposes a diffusion model adaptation method combined with particle filtering and integrated into a pre-trained image-to-image diffusion model to further improve the restoration quality.

[0105] (3) The system framework of the present invention can have good low-sampling image restoration capabilities for the diffusion model. And the framework can be used in any scenario. It only needs to be fully deployed in a fine-tuning manner to complete customization to meet the needs of different scenarios. In addition, the method of the present invention only needs to collect images and fine-tune them, which is highly reproducible and can lay the foundation for subsequent real-time testing.

[0106] Example 2

[0107] This embodiment provides a controllable recovery device for a low-sampling spectrum situation map, including:

[0108] Image acquisition module, acquiring low-sampled images;

[0109] The interpolation adaptation module uses different interpolation methods to restore the low-sampled image to enhance the weak prior of the appearance in the image generation process and obtain images with different degrees of interpolation adaptation;

[0110] The image filtering module filters the adapted image to find a more satisfactory image and converts the found image into a conditional image of the image-generated image;

[0111] The image restoration module inputs the conditional image into the diffusion model based on particle filtering for processing and outputs the final restored image.

[0112] Since the device is a controllable recovery device for a low-sampling spectrum situation diagram of an embodiment of the present invention, and the principle of solving the problem by the device is similar to that of the method, the implementation of the device can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0113] Example 3

[0114] An embodiment of the present invention further provides an electronic device, the electronic device comprising a processor and a memory, the memory storing at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement the following Figure 5 A controllable recovery method for a low-sampling spectrum situation map is shown.

[0115] It is understood that the memory may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory may be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data created according to the use of the server, etc.

[0116] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor can integrate one or a combination of a central processing unit (CPU) and a modem. Among them, the CPU mainly processes the operating system and application programs; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but implemented separately through a chip.

[0117] Since the electronic device is an electronic device corresponding to a controllable recovery method for a low-sampling spectrum situation diagram in an embodiment of the present invention, and the principle of solving the problem by the electronic device is similar to that of the method, the implementation of the electronic device can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0118] Example 4

[0119] The embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the following Figure 5 A controllable recovery method for a low-sampling spectrum situation map is shown.

[0120] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0121] Since the storage medium is a storage medium corresponding to a controllable recovery method for a low-sampling spectrum situation map in an embodiment of the present invention, and the principle of solving the problem by the storage medium is similar to that of the method, the implementation of the storage medium can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0122] Example 5

[0123] In some possible implementations, various aspects of the method of the embodiment of the present invention can also be implemented in the form of a program product, which includes a program code. When the program product is run on a computer device, the program code is used to enable the computer device to execute the steps of a controllable recovery method of a low-sampling spectrum situation map according to various exemplary embodiments of the present application described above in this specification. Among them, the executable computer program code or "code" used to execute each embodiment can be written in a high-level programming language such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0124] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0125] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0126] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A controllable recovery method for a low-sampling spectrum situation map, characterized in that: The following steps are involved: Get a downsampled image; The low-sampled image is restored using different interpolation methods to enhance the weak prior of appearance in the image generation process. Obtain images with different degrees of interpolation adaptation; Filter and find a more satisfactory image from the adapted image, and convert the found image into a conditional image of the image-generated image; The conditional image is input into the diffusion model based on particle filtering for processing, and the final restored image is output.

2. The method for controllable recovery of low-sampling spectrum situation map according to claim 1, characterized in that: The step of inputting the conditional image into a diffusion model based on particle filtering for processing and outputting a final restored image comprises: Set the time step N and particle size S; When the diffusion model is reversed, at each time step t changes from N to 1, an average sample is generated from the distribution Through the particle filtering method, from the sample Randomly select S middle samples as replacement samples; These S replacement samples are represented as From S samples with replacement The final satisfactory image χ0 is obtained.

3. The method for controllable recovery of low-sampling spectrum situation map according to claim 2, characterized in that: The particle filter method works as follows: Initialization and state prediction of particles: The initial particles represent the initial hypothetical state of the data; in the image restoration task, the particles represent the initial low-quality or noisy image; according to the state transition model, the state of the particles is predicted, that is, the particles are allowed to evolve in the next diffusion process; The forward process of the diffusion model: gradually add noise to the hypothetical state of the particle, so that the state gradually diffuses to a completely random state; Particle weight update: In each diffusion step, the particle weight is updated based on the observation and the current particle state; Resampling and replacement: When the weights of some particles are low, particles are regenerated through the resampling mechanism; Reverse diffusion process: In the reverse process of the diffusion model, particles gradually remove noise based on the observed data; Particle filtering controls the denoising process by estimating the state of particles, making the final state closer to the actual target data.

4. The method for controllable recovery of low-sampling spectrum situation map according to claim 1, characterized in that: Different interpolation methods include nearest neighbor, ordinary kriging, or radial basis functions.

5. The method for controllable recovery of low-sampling spectrum situation graph according to claim 1, characterized in that: The method of restoring the low-sampled image by using different interpolation methods includes: Select appropriate interpolation method: According to the specific characteristics and data distribution of the low-sampled image, select one or more interpolation methods for restoration; Fusion of multiple interpolation methods: Combining multiple interpolation methods to generate multiple restored versions of the image, thereby improving the diversity of restoration.

6. A controllable restoration method of a low-sampling spectrum situation map according to claim 5, characterized in that: The selecting one or more interpolation methods for restoration includes: For simple scenes, select the nearest neighbor interpolation method for restoration; for complex or random scenes, select the Kriging interpolation method or the radial basis function interpolation method for restoration.

7. The method for controllable recovery of low-sampling spectrum situation diagram according to claim 1, characterized in that: The filtering and finding a more satisfactory image from the adapted image includes: Index screening criteria: Screening the images generated by interpolation according to the predetermined index evaluation criteria; Automatic scoring mechanism: Automatically score multiple interpolated images and select the image that is closest to the real scene; Final screening: Based on the index screening criteria or automatic scoring results, one or more images are selected as the initial conditions for subsequent processing.

8. A controllable recovery device for low-sampling spectrum situation diagram, characterized in that: include: Image acquisition module, acquiring low-sampled images; The interpolation adaptation module uses different interpolation methods to restore the low-sampled image to enhance the weak prior of the appearance in the image generation process and obtain images with different degrees of interpolation adaptation; The image filtering module filters the adapted image to find a more satisfactory image and converts the found image into a conditional image of the image-generated image; The image restoration module inputs the conditional image into the diffusion model based on particle filtering for processing and outputs the final restored image.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 7.

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