Super-resolution infrared spectrum imaging method and device

Abstract

The invention provides a super-resolution infrared spectrum imaging method and device, and the method comprises the steps: obtaining a needle point change signal and a relative position signal of a sample and a needle point when a sample and the needle point interact under the excitation of infrared light; superposing the needle tip change signal and the relative position signal, performing infrared imaging, and repeatedly scanning a set area of the sample to obtain an image data set; aligning the image data set according to features to obtain an aligned image; carrying out superposition and average processing on the aligned image to obtain a processed image; carrying out peak searching processing on the processed image by adopting a peak detection algorithm to obtain a peak site of the feature structure; performing image reconstruction on the peak value site by adopting a point spread function to obtain a nano infrared image; by applying a positioning algorithm to an infrared image, the problems of insufficient spatial resolution, thermal drift and noise interference generally existing in a nano infrared technology are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical analysis, and in particular to a super-resolution infrared spectrum imaging method and device. Background Art

[0002] In existing nano-infrared imaging techniques, the tip convolution effect is a key factor affecting image resolution. The tip's geometry interacts with the sample surface, causing image distortion and limiting resolution. Furthermore, signal instability and noise further compromise image quality, making it difficult to obtain clear and accurate structural information from complex samples.

[0003] Currently, these issues can be partially alleviated by optimizing the tip shape and using ultra-sharp probes, but these methods still have limitations in practical applications, especially when processing soft materials and dynamic samples. The application of peak force tapping mode reduces the convolution effect, but the contact area wave induced by thermodynamic drift and the limitation of mechanical response still remain. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a super-resolution infrared spectral imaging method and device, which solves the problems of insufficient spatial resolution, thermal drift and noise interference that are common in nano-infrared technology by applying a positioning algorithm to infrared images.

[0005] In a first aspect, an embodiment of the present invention provides a super-resolution infrared spectral imaging method, the method comprising:

[0006] When the infrared light is excited and the sample and the needle tip interact, a signal of the change of the needle tip and a signal of the relative position of the sample and the needle tip are obtained;

[0007] performing infrared imaging after superimposing the needle tip change signal and the relative position signal, and repeatedly scanning a set area of ​​the sample to obtain an image data set;

[0008] Aligning the image dataset according to features to obtain an aligned image;

[0009] Superimposing and averaging the aligned images to obtain a processed image;

[0010] The processed image is subjected to peak detection processing using a peak detection algorithm to obtain peak locations of characteristic structures;

[0011] The peak position is image reconstructed using a point spread function to obtain a nano-infrared image.

[0012] Furthermore, the image dataset includes a plurality of images, and aligning the image dataset according to features to obtain aligned images includes:

[0013] Preprocessing the image to obtain a preprocessed image;

[0014] Performing normalized cross-correlation matching on the preprocessed images frame by frame, and calculating the similarity between each frame of the image and the reference image;

[0015] Determining an offset according to a similarity between each frame of image and the reference image;

[0016] Based on the offset, the image is aligned with the reference image to obtain the aligned image.

[0017] Furthermore, the aligned images are superimposed and averaged to obtain a processed image, including:

[0018] The values ​​of each pixel in the aligned image are weighted and summed, and then averaged to obtain the processed image.

[0019] Furthermore, weighted summing and averaging the values ​​of each pixel in the aligned image are performed to obtain the processed image, including:

[0020] The processed image is calculated according to the following formula:

[0021]

[0022] Wherein, N is the number of images, I i (x, y) represents the pixel value of the i-th image at (x, y), is the processed image.

[0023] Furthermore, the processed image is subjected to peak detection processing using a peak detection algorithm to obtain peak locations of characteristic structures, including:

[0024] Scanning each pixel in the processed image;

[0025] comparing the value of each said pixel with the values ​​of pixels in a surrounding neighborhood;

[0026] If the value of the current pixel is greater than the pixel value of the surrounding neighborhood and greater than a set threshold, the position of the current pixel is used as the peak position of the characteristic structure.

[0027] Furthermore, the peak position of the characteristic structure is determined by:

[0028] and

[0029] Wherein, N(x,y) is the surrounding neighborhood centered on (x,y), T is the set threshold, I(x,y) is the value of each pixel at (x,y), and (x',y')∈N(x,y) is the position (x',y') of each pixel in the surrounding neighborhood.

[0030] Furthermore, the point spread function is implemented in the following manner:

[0031]

[0032] where I(r) is the signal intensity at a distance r from the center, J1 is the Bessel function of the first kind, λ is the wavelength, f is the characteristic length associated with the interaction between the probe and the sample, which is related to the focal length, and r is the distance from the center to the considered point.

[0033] In a second aspect, an embodiment of the present invention provides a super-resolution infrared spectral imaging device, the device comprising:

[0034] An interaction module, configured to obtain a signal indicating a change in the needle tip and a signal indicating a relative position of the sample and the needle tip when the sample and the needle tip interact with each other due to infrared light excitation;

[0035] a scanning module, configured to perform infrared imaging after superimposing the needle tip change signal and the relative position signal, and repeatedly scan a set area of ​​the sample to obtain an image data set;

[0036] An alignment module, configured to align the image dataset according to features to obtain an aligned image;

[0037] an averaging processing module, configured to perform superposition and averaging processing on the aligned images to obtain a processed image;

[0038] A peak detection processing module is used to perform peak detection processing on the processed image using a peak detection algorithm to obtain the peak position of the characteristic structure;

[0039] The image reconstruction module is used to reconstruct the image of the peak position using a point spread function to obtain a nano-infrared image.

[0040] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the above-mentioned method when executing the computer program.

[0041] In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having a non-volatile program code executable by a processor, wherein the program code enables the processor to execute the method as described above.

[0042] An embodiment of the present invention provides a super-resolution infrared spectral imaging method and device, including: when infrared light excitation causes the sample and the needle tip to interact, a needle tip change signal and a relative position signal of the sample and the needle tip are obtained; the needle tip change signal and the relative position signal are superimposed to perform infrared imaging, and a set area of ​​the sample is repeatedly scanned to obtain an image data set; the image data set is aligned according to features to obtain an aligned image; the aligned images are superimposed and averaged to obtain a processed image; the processed image is peak-searched using a peak detection algorithm to obtain the peak position of the characteristic structure; the peak position is image-reconstructed using a point spread function to obtain a nano-infrared image; by applying the positioning algorithm to the infrared image, the problems of insufficient spatial resolution, thermal drift and noise interference that are common in nano-infrared technology are solved.

[0043] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 Flowchart of the super-resolution infrared spectroscopy imaging method provided in Example 1 of the present invention;

[0047] Figure 2 Schematic diagram of the super-resolution infrared spectroscopy imaging process provided in Example 1 of the present invention;

[0048] Figure 3 A schematic diagram of the super-resolution infrared spectroscopy imaging process provided in Example 1 of the present invention;

[0049] Figure 4A schematic diagram of another super-resolution infrared spectral imaging process provided in Example 1 of the present invention;

[0050] Figure 5 Schematic diagram of the process of applying the positioning algorithm provided in Example 1 of the present invention to an infrared image of PMMA;

[0051] Figure 6 Schematic diagram of a super-resolution infrared spectral imaging device provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] The existing nano-infrared microscope imaging technology has the following main problems: insufficient spatial resolution:

[0054] Limited spatial resolution: In nano-infrared imaging technology, the probe achieves surface and interface chemical characterization through point-by-point scanning. Theoretically, the best imaging effect is achieved when the tip is infinitely small. However, due to process technology and practical limitations, if the tip is made too small, the detection sensitivity will be limited, and the stability of the signal cannot be guaranteed. Therefore, the size of the general tip reaches tens of nanometers, and the tip convolution effect will occur. When imaging below ten nanometers, it is affected by the tip convolution. When the size of the scanned surface feature is close to or smaller than the tip size, aliasing of the height signal and the infrared signal will occur. The imaging system cannot present the true structure of the sample, but instead presents it in a broadened form, and the spatial resolution of the image is reduced. This not only makes it impossible to identify the sample structure, but also introduces special distortion phenomena such as blurred chemical boundaries and local contrast anomalies.

[0055] Thermal drift: In nano-infrared imaging, thermal drift refers to minute shifts in the position of the device or sample during the imaging process due to temperature changes in the sample and probe. Temperature fluctuations can affect the interaction between the probe and sample, leading to image distortion or blurring. Thermal drift typically manifests as displacement or irregularities in the image, especially during long scans. This phenomenon can adversely affect image accuracy, particularly when performing high-resolution imaging.

[0056] Interference distortion: In nano-infrared imaging, rare interference typically refers to sporadic noise or other distortion phenomena, which can be caused by a variety of factors. Although these interferences are infrequent in most experiments, they can still affect imaging quality and reduce the accuracy of sample analysis.

[0057] The specific reasons for the above shortcomings are:

[0058] Tip convolution phenomenon: When the sample size is close to or smaller than the tip size, the tip is affected by the aliasing of signals from different positions of the sample, resulting in broadening of the sample signal and a decrease in spatial resolution.

[0059] Temperature variation: Due to temperature changes in the sample and probe, the position of the device or sample may drift slightly. Temperature fluctuations affect the interaction between the probe and the sample, resulting in thermal drift in the image.

[0060] Noise and other interferences: These interferences may come from factors such as system noise, atmospheric absorption characteristics, target radiation characteristics, and grayscale fluctuations of infrared detectors.

[0061] The positioning atomic force microscope image processing technology described in this application represents a cutting-edge advancement in nano-infrared image enhancement technology, as there is currently no precedent for applying positioning algorithms to nano-infrared images. With the development of nano-infrared spectroscopy, infrared technology based on atomic force microscopy has achieved significant breakthroughs in spatial resolution and sensitivity. However, current nano-infrared imaging technology is still constrained by issues such as tip convolution, signal instability, and noise, making it difficult to achieve higher spatial resolution.

[0062] This application introduces a positioning algorithm into nano-infrared images. By applying the algorithm to infrared images, it successfully solves the problem of insufficient spatial resolution that is prevalent in nano-infrared technology, further expands the application range of the instrument, and provides a super-resolution imaging analysis tool for the fields of life sciences, materials characterization, and environmental monitoring.

[0063] To facilitate understanding of this embodiment, the embodiment of the present invention is described in detail below.

[0064] Example 1:

[0065] Figure 1 This is a flow chart of the super-resolution infrared spectral imaging method provided in Example 1 of the present invention.

[0066] Reference Figure 1 , the method comprises the following steps:

[0067] Step S101, when infrared light excites and causes the sample and the needle tip to interact, a needle tip change signal and a relative position signal of the sample and the needle tip are obtained;

[0068] Step S102, superimposing the needle tip change signal and the relative position signal to perform infrared imaging, and repeatedly scanning a set area of ​​the sample to obtain an image data set;

[0069] Step S103, aligning the image data set according to the features to obtain an aligned image;

[0070] Step S104, superimposing and averaging the aligned images to obtain a processed image;

[0071] Step S105, performing peak detection processing on the processed image using a peak detection algorithm to obtain the peak position of the characteristic structure;

[0072] Step S106 , performing image reconstruction on the peak position using a point spread function to obtain a nano-infrared image.

[0073] Specifically, refer to Figure 2 The positioning algorithm breaks through the traditional spatial resolution limit. The main control unit controls the excitation of infrared light and the probe sample relative controller. The infrared light stimulates the interaction between the sample and the needle tip. The relative position signal of the sample and the needle tip is fed back to the probe sample relative controller to control the position between the sample and the needle tip.

[0074] The needle tip's changing signals are detected by a four-image detector. A lock-in amplifier superimposes the needle tip's changing signals and relative position signals, generating infrared imaging. Randomly scanning a set area, the images are captured and aligned based on features using the normalized cross-correlation algorithm (normxcorr2). The algorithm first preprocesses the input image; then, normalized cross-correlation matching is performed frame by frame. The offset is determined by calculating the similarity between each frame and a reference image. This offset is then used to align the image set with the selected reference image.

[0075] The characteristic area of ​​the image is fed back to the main control machine. If the image drifts too much, the relative position controller of the probe sample is manually calibrated. After the image data sets are aligned according to the characteristics, they are superimposed, that is, the value of each pixel is weighted and summed. In this way, the same signal components are enhanced in multiple images, while occasional interference or noise is effectively averaged out, thereby improving the signal-to-noise ratio of the image. For details, refer to formula (1). The processed image is processed using a peak detection algorithm to obtain the peak position of the characteristic structure; the peak position is reconstructed using a point spread function to obtain a nano-infrared image.

[0076] Furthermore, the image dataset includes multiple images, and step S103 includes the following steps:

[0077] Step S201, preprocessing the image to obtain a preprocessed image;

[0078] Step S202, performing normalized cross-correlation matching on the pre-processed images frame by frame, and calculating the similarity between each frame of the image and the reference image;

[0079] Step S203, determining an offset according to the similarity between each frame image and the reference image;

[0080] Step S204: aligning the image with the reference image based on the offset to obtain an aligned image.

[0081] Furthermore, step S104 includes the following steps:

[0082] The values ​​of each pixel in the aligned image are weighted and summed, and then averaged to obtain the processed image.

[0083] Furthermore, step S104 includes:

[0084] The processed image is calculated according to formula (1):

[0085]

[0086] Where N is the number of images, I i (x,y) represents the pixel value of the i-th image at (x,y), is the processed image.

[0087] Furthermore, step S105 includes the following steps:

[0088] Step S301, scanning each pixel in the processed image;

[0089] Step S302, comparing the value of each pixel with the pixel values ​​of the surrounding neighborhood;

[0090] Step S303: If the value of the current pixel is greater than the pixel values ​​of the surrounding neighborhood and greater than the set threshold, the position of the current pixel is used as the peak position of the feature structure.

[0091] Furthermore, the peak position of the characteristic structure is determined by the following method, referring to formula (2):

[0092]

[0093] Where N(x,y) is the surrounding neighborhood centered on (x,y), T is the set threshold, I(x,y) is the value of each pixel at (x,y), and (x',y')∈N(x,y) is the position (x',y') of each pixel in the surrounding neighborhood.

[0094] Specifically, the processed image is processed by a peak detection algorithm. The principle of the peak detection algorithm is to identify the points in the image where the local brightness value exceeds the set threshold, that is, the peak position in the image. These peaks usually represent the positions of objects or particles in the image. The peak detection algorithm scans each pixel of the image, compares the pixel values ​​of its surrounding neighborhood, finds the local maximum value that is significantly higher than the surrounding neighborhood, and determines these positions as potential particle positions. Referring to formula (2), max is the comparison value calculated with the neighboring pixels. If the value of a pixel is greater than the pixel value of the surrounding neighborhood and exceeds the set threshold, it is selected as the peak position.

[0095] Furthermore, the point spread function is implemented in the following way, referring to formula (3):

[0096]

[0097] where I(r) is the signal intensity at a distance r from the center, J1 is the Bessel function of the first kind, λ is the wavelength, f is the characteristic length associated with the interaction between the probe and the sample, which is related to the focal length, and r is the distance from the center to the considered point.

[0098] Specifically, when the peak position is found, the point spread function (PSF) is combined to obtain a super-resolution nano-infrared image. The point spread function describes the diffraction pattern formed by a point light source on the imaging plane.

[0099] Reference Figure 3 Schematic diagram of super-resolution infrared spectral imaging process. Figure 4 In Figure (a), the same set area is first imaged, and drift is inevitable during the imaging process; Figure (b) feeds back to the main control computer to calibrate the drift of the image. After collecting data, in Figure (c), the sub-pixel level registration algorithm based on the feature structure is used to achieve precise image alignment; then, as shown in Figure (d), the images are superimposed and averaged, and the peak position of each image is extracted by finding the local maximum value, as shown in Figure (e), and the peak is determined by statistics; the point spread function is used to reconstruct the point spread function image of the obtained peak position, as shown in Figure (f), and finally a super-resolution image is obtained.

[0100] This application is the first to extend this localization algorithm to the field of AFM-IR, developing LAFM-IR technology. Experiments have shown that this method improves the spatial resolution of AFM-IR and successfully resolves the infrared vibrational signatures of individual peptide molecules. This technological fusion opens up new avenues for nanoscale chemical imaging, particularly in the study of biomacromolecule interface interactions.

[0101] To demonstrate the applicability of the localization algorithm to nanoscale infrared imaging, the algorithm was applied to a standard block copolymer sample. The block copolymer PMMA-b-PS (polymethyl methacrylate-b-polystyrene) is a diblock copolymer composed of two chemically distinct polymer segments, polymethyl methacrylate (PMMA) and polystyrene (PS), connected by covalent bonds. This structure endows PMMA-b-PS with unique physical and chemical properties, enabling it to excel in a variety of applications.

[0102] The positioning algorithm was applied to the infrared image of PMMA and the spatial resolution of the infrared image of PMMA was successfully improved.

[0103] exist Figure 5 Figure (a) shows an AFM topographic image of PMMA; Figure (b) shows the corresponding PMMA structure and infrared spectrum. Infrared light at a wavelength of 1728 cm⁻¹ was used for excitation. Eighty images corresponding to the topography in Figure (a) were aligned and reconstructed. Due to convolution of the tip, many details are lost in the pre-processed image (Figure (c). However, after reconstruction using the positioning algorithm, more details are visible, as shown in Figure (d).

[0104] When checking the signal intensity at the same position, as shown in Figure (e), the solid line is the intensity line before processing, and the dotted line is the intensity line after processing. It is observed that the spatial resolution of the image is significantly improved, and the signal-to-noise ratio of the image is significantly improved. Before processing, the signal line intensity was interfered with by noise and the signal was not obvious. After processing, the signal intensity position shown by the profile line is more accurate and is not interfered with.

[0105] Select the box in Figure (c) and zoom in, as shown in Figure (f). The positioning algorithm reconstructs the signal line, as shown in Figure (g). The signal line intensity is shown in Figure (h). The solid line represents the intensity line before processing, and the dashed line represents the intensity line after processing. Spatial resolution is significantly improved. Signal intensity is higher in areas with signal than in the unprocessed sample, and the difference between peaks and troughs is more pronounced, indicating that the structure is easier to distinguish. The results for the standard sample demonstrate the significant improvement of the positioning algorithm's ability to enhance nano-infrared technology, validating the feasibility of this research approach.

[0106] During the experiment, the effects of thermal drift were effectively eliminated through precise preprocessing steps. The images were aligned multiple times, accurately aligning them based on multiple structural features within the image, thus reducing image shifts caused by temperature fluctuations or equipment instability.

[0107] At the same time, random interferences during the experiment, such as noise and electromagnetic interference, are removed through localization algorithms and image processing techniques. These preprocessing steps effectively eliminate the influence of background noise, making the final image clearer and more accurate, better reflecting the true structure and molecular characteristics of the sample. This provides a reliable foundation for subsequent super-resolution imaging and precise analysis.

[0108] This application proposes a nano-infrared image enhancement technology based on a positioning algorithm to solve the problems of insufficient spatial resolution, thermal drift and noise interference in the existing technology.

[0109] Breaking through spatial resolution limitations: The spatial resolution of nano-infrared imaging technology is limited by the tip convolution effect, making it unable to meet the imaging requirements of some nanoscale samples. By incorporating positioning algorithms into nano-infrared imaging technology, the spatial resolution of nano-infrared images has been successfully improved, enabling imaging and characterization of samples at different nanoscales, and observing features of structures even finer than those in the original image.

[0110] Eliminating thermal drift: In nano-infrared imaging, temperature changes in the sample and probe can cause slight drift in the position of the device or sample. Temperature fluctuations can affect the interaction between the probe and the sample, leading to image distortion or blurring.

[0111] Before implementing localization algorithms to obtain super-resolution images, the images must first be calibrated and aligned. This process identifies features within the image and aligns multiple images to ensure that all images can be compared and analyzed based on the same feature locations. This operation is primarily intended to reduce image distortion caused by thermal drift.

[0112] Removing noise and other interference: In nano-infrared imaging, rare interference typically refers to sporadic noise or other distortions, which can be caused by a variety of factors. While these interferences are infrequent in most experiments, they can still affect imaging quality and reduce the accuracy of analyses of samples such as cancer cells.

[0113] Because noise and interference occur randomly, they can affect image quality. By acquiring a large number of image samples and then aligning and averaging them, we can effectively reduce occasional interference artifacts. Alignment ensures that all images are compared based on the same features, while averaging cancels out these random interference artifacts across multiple images, removing unnecessary noise.

[0114] Example 2:

[0115] Figure 6 Schematic diagram of a super-resolution infrared spectral imaging device provided in Example 2 of the present invention.

[0116] Reference Figure 6 , the device comprises:

[0117] The interaction module is used to obtain a signal of the change in the needle tip and a signal of the relative position of the sample and the needle tip when the sample and the needle tip interact with each other due to infrared light excitation;

[0118] The scanning module is used to perform infrared imaging after superimposing the needle tip change signal and the relative position signal, and repeatedly scan the set area of ​​the sample to obtain an image data set;

[0119] The alignment module is used to align the image dataset according to the features to obtain the aligned image;

[0120] An averaging processing module is used to perform superposition and averaging processing on the aligned images to obtain a processed image;

[0121] The peak detection processing module is used to perform peak detection on the processed image using a peak detection algorithm to obtain the peak position of the characteristic structure;

[0122] The image reconstruction module is used to reconstruct the image of the peak position using a point spread function to obtain a nano-infrared image.

[0123] This application uses a nano-infrared image enhancement technology based on a positioning algorithm to achieve super-resolution of nano-infrared images; combined with the positioning algorithm, the spatial resolution of nano-infrared images at a smaller scale is achieved, breaking through the spatial resolution of existing nano-infrared images; through precise alignment, the displacements caused by temperature changes or unstable interactions between the probe and the sample can be effectively eliminated, thereby ensuring the accuracy of the image data (thermal drift elimination); by acquiring a large amount of image sample data and aligning and averaging these images, occasional interference distortion can be effectively reduced (interference elimination).

[0124] An embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the super-resolution infrared spectral imaging method provided in the above embodiment are implemented.

[0125] An embodiment of the present invention further provides a computer-readable medium having a non-volatile program code executable by a processor, wherein a computer program is stored on the computer-readable medium. When the computer program is executed by the processor, the steps of the super-resolution infrared spectral imaging method of the above embodiment are executed.

[0126] The computer program product provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. For specific implementation, please refer to the method embodiments and will not be repeated here.

[0127] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and devices can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0128] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0129] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0130] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0131] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A super-resolution infrared spectroscopy imaging method, characterized in that: The method comprises: When the infrared light is excited and the sample and the needle tip interact, a signal of the change of the needle tip and a signal of the relative position of the sample and the needle tip are obtained; performing infrared imaging after superimposing the needle tip change signal and the relative position signal, and repeatedly scanning a set area of ​​the sample to obtain an image data set; Aligning the image dataset according to features to obtain an aligned image; Superimposing and averaging the aligned images to obtain a processed image; The processed image is subjected to peak detection processing using a peak detection algorithm to obtain peak locations of characteristic structures; The peak position is image reconstructed using a point spread function to obtain a nano-infrared image.

2. The super-resolution infrared spectroscopy imaging method according to claim 1, wherein The image dataset includes a plurality of images, and the image dataset is aligned according to features to obtain aligned images, including: Preprocessing the image to obtain a preprocessed image; Performing normalized cross-correlation matching on the preprocessed images frame by frame, and calculating the similarity between each frame of the image and the reference image; Determining an offset according to a similarity between each frame of image and the reference image; Based on the offset, the image is aligned with the reference image to obtain the aligned image.

3. The super-resolution infrared spectroscopy imaging method according to claim 1, characterized in that: The aligned images are superimposed and averaged to obtain a processed image, comprising: The values ​​of each pixel in the aligned image are weighted and summed, and then averaged to obtain the processed image.

4. The super-resolution infrared spectroscopy imaging method according to claim 3, characterized in that: The weighted sum of the values ​​of each pixel in the aligned image is then averaged to obtain the processed image, including: The processed image is calculated according to the following formula: Wherein, N is the number of images, I i (x, y) represents the pixel value of the i-th image at (x, y), is the processed image.

5. The super-resolution infrared spectroscopy imaging method according to claim 1, characterized in that: The processed image is subjected to peak detection processing using a peak detection algorithm to obtain peak locations of characteristic structures, including: Scanning each pixel in the processed image; comparing the value of each said pixel with the values ​​of pixels in the surrounding neighborhood; If the value of the current pixel is greater than the pixel value of the surrounding neighborhood and greater than a set threshold, the position of the current pixel is used as the peak position of the characteristic structure.

6. The super-resolution infrared spectroscopy imaging method according to claim 5, characterized in that: The peak position of the characteristic structure is determined by the following method: and Wherein, N(x,y) is the surrounding neighborhood centered on (x,y), T is the set threshold, I(x,y) is the value of each pixel at (x,y), and (x',y')∈N(x,y) is the position (x',y') of each pixel in the surrounding neighborhood.

7. The super-resolution infrared spectroscopy imaging method according to claim 1, characterized in that: The point spread function is implemented in the following way: where I(r) is the signal intensity at a distance r from the center, J1 is the Bessel function of the first kind, λ is the wavelength, f is the characteristic length associated with the interaction between the probe and the sample, which is related to the focal length, and r is the distance from the center to the considered point.

8. A super-resolution infrared spectral imaging device, characterized in that: The device comprises: An interaction module, configured to obtain a signal indicating a change in the needle tip and a signal indicating a relative position of the sample and the needle tip when the sample and the needle tip interact with each other due to infrared light excitation; a scanning module, configured to perform infrared imaging after superimposing the needle tip change signal and the relative position signal, and repeatedly scan a set area of ​​the sample to obtain an image data set; An alignment module, configured to align the image dataset according to features to obtain an aligned image; an averaging processing module, configured to perform superposition and averaging processing on the aligned images to obtain a processed image; A peak detection processing module is used to perform peak detection processing on the processed image using a peak detection algorithm to obtain the peak position of the characteristic structure; The image reconstruction module is used to reconstruct the image of the peak position using a point spread function to obtain a nano-infrared image.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable medium having a non-volatile program code executable by a processor, characterized in that The program code causes the processor to execute the method according to any one of claims 1 to 7.

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