A method for enhancing a terahertz near-field radar image

Abstract

The application discloses a kind of enhancement methods of terahertz near-field radar image, comprising the following steps: the data conversion of the terahertz near-field radar image to be enhanced is normalized, and normalized terahertz near-field radar image is obtained;The normalized terahertz near-field radar image is decomposed using stationary wavelet transform, and a plurality of low-frequency subbands and a plurality of high-frequency subbands are obtained;The high-frequency multi-direction filter of high-frequency subband is carried out using different direction filters, and a plurality of enhanced high-frequency subbands are obtained;The low-frequency adaptive linear enhancement of low-frequency subband is carried out using the mean and standard deviation of low-frequency subband, and a plurality of enhanced low-frequency subbands are obtained;The inverse stationary wavelet transform is carried out to a plurality of enhanced high-frequency subbands and a plurality of enhanced low-frequency subbands, and reconstructed terahertz near-field radar image is obtained;Linear transformation is carried out to reconstructed terahertz near-field radar image, and enhanced terahertz near-field radar image is obtained.The application can enhance contrast, highlight edge features and rich detail information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image enhancement, in particular to a method for enhancing a terahertz near-field radar image. BACKGROUND

[0002] At present, the terahertz wave has important application value in target detection field due to its outstanding material penetration ability and anti-interference characteristics in the electromagnetic spectrum, and the advantage that the propagation process is less affected by atmospheric conditions. Compared with the traditional microwave radar system, the terahertz radar system has wider working bandwidth, higher spatial resolution and stronger Doppler effect sensitivity, and has become a leading technology for modern target detection, trajectory tracking and feature recognition. With the continuous iteration of terahertz imaging algorithm, relatively clear imaging results can be achieved at present, which provides a basis for subsequent image processing.

[0003] However, due to the insufficient intensity of the terahertz wave light source and the difference in reflectivity of the target material, the existing imaging results generally have problems such as low contrast, blurred edge features and weak details. At the same time, since the target and the background have similar brightness distribution in the terahertz image, when the background brightness is close to the target, the feature information is easily covered, which seriously affects the accuracy of subsequent target detection and feature recognition. The current image enhancement algorithm mainly includes two categories: the method based on deep learning (such as super-resolution reconstruction and generative adversarial network) can effectively reconstruct the detail features, but it is limited by the lack of training samples due to the high cost of the terahertz imaging system, which easily causes model convergence difficulty and non-realistic artifacts; the traditional method (such as histogram equalization, Gaussian filtering, mean filtering and bilateral filtering) can improve the contrast or suppress the noise, but it will cause problems such as excessive enhancement of noise, overexposure / underexposure of local area, edge blur and loss of high-frequency details, which cannot meet the high-quality enhancement requirements of the terahertz near-field radar image.

[0004] Therefore, how to provide a method for enhancing a terahertz near-field radar image, which can enhance the contrast of the terahertz near-field radar image to be enhanced, highlight the edge features of the terahertz near-field radar image to be enhanced, and enrich the detail information of the terahertz near-field radar image to be enhanced, is a problem that those skilled in the art need to solve. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a method for enhancing a terahertz near-field radar image.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] A method for enhancing a terahertz near-field radar image, comprising the following steps:

[0008] S1: data conversion and normalization are performed on a to-be-enhanced terahertz near-field radar image to obtain a normalized terahertz near-field radar image;

[0009] S2: stationary wavelet transform decomposition is performed on the normalized terahertz near-field radar image to obtain a plurality of low-frequency subbands and a plurality of high-frequency subbands;

[0010] S3: high-frequency multi-directional filtering is performed on the high-frequency subbands by using filters in different directions to obtain a plurality of enhanced high-frequency subbands;

[0011] Low-frequency adaptive linear enhancement is performed on the low-frequency subbands by using the mean and standard deviation of the low-frequency subbands to obtain a plurality of enhanced low-frequency subbands;

[0012] S4: inverse stationary wavelet transform is performed on the plurality of enhanced high-frequency subbands and the plurality of enhanced low-frequency subbands to obtain a reconstructed terahertz near-field radar image;

[0013] S5: linear transformation is performed on the reconstructed terahertz near-field radar image to obtain an enhanced terahertz near-field radar image.

[0014] Preferably, the data conversion in S1 converts the pixel values of all pixels of the to-be-enhanced terahertz near-field radar image from 8-bit unsigned integers to 32-bit floating-point numbers;

[0015] The normalization in S1 linearly maps the pixel values of all pixels of the to-be-enhanced terahertz near-field radar image after data conversion from the [0, 255] interval to the [0, 1] interval.

[0016] Preferably, S2 specifically includes the following steps:

[0017] S21: first-level decomposition is performed on the normalized terahertz near-field radar image by using the low-pass filter and the high-pass filter of the Haar filter bank to obtain a low-frequency subband LL1, a horizontal high-frequency subband LH1, a vertical high-frequency subband HL1, and a diagonal high-frequency subband HH1;

[0018] S22: second-level decomposition is performed on the low-frequency subband LL1 by using the low-pass filter and the high-pass filter of the Haar filter bank to obtain a low-frequency subband LL2, a horizontal high-frequency subband LH2, a vertical high-frequency subband HL2, and a diagonal high-frequency subband HH2.

[0019] Preferably, the calculation formulas of the low-frequency subband LL1, the horizontal high-frequency subband LH1, the vertical high-frequency subband HL1, the diagonal high-frequency subband HH1, the low-frequency subband LL2, the horizontal high-frequency subband LH2, the vertical high-frequency subband HL2, and the diagonal high-frequency subband HH2 are as follows:

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] wherein, represents the normalized terahertz near-field radar image; and represent low-pass filter and high-pass filter of the Haar filter bank respectively; represents convolution operation; T represents transposition.

[0029] Preferably, high-frequency multi-directional filtering is performed on the high-frequency subband by using filters of different directions to obtain several enhanced high-frequency subbands, which is realized based on the following formula:

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] wherein, represents convolution operation; K r represents the rth direction filter, r = 1, 2,..., 8, K1 represents horizontal direction filter, K2 represents vertical direction filter, K3 represents 45° diagonal direction filter, K4 represents 135° diagonal direction filter, K5 represents horizontal edge enhancement filter, K6 represents vertical edge enhancement filter, K7 represents Laplace filter, and K8 represents sharpening filter; represents high-frequency multi-directional filtering on the horizontal high-frequency subband LH1 to obtain an enhanced horizontal high-frequency subband; represents a high-frequency multi-directional filtering on the vertical high-frequency subband HL1, and an enhanced vertical high-frequency subband obtained thereby; represents a high-frequency multi-directional filtering on the diagonal high-frequency subband HH1, and an enhanced diagonal high-frequency subband obtained thereby; represents a high-frequency multi-directional filtering on the horizontal high-frequency subband LH2, and an enhanced horizontal high-frequency subband obtained thereby; represents a high-frequency multi-directional filtering on the vertical high-frequency subband HL2, and an enhanced vertical high-frequency subband obtained thereby; represents a high-frequency multi-directional filtering on the diagonal high-frequency subband HH2, and an enhanced diagonal high-frequency subband obtained thereby.

[0037] Preferably, low-frequency adaptive linear enhancement is performed on the low-frequency subbands by using the mean and standard deviation of the low-frequency subbands, and a plurality of enhanced low-frequency subbands are obtained, which are realized based on the following formula:

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] ;

[0054] wherein M represents the number of rows of the low-frequency subband LL1 and the low-frequency subband LL2; N represents the number of columns of the low-frequency subband LL1 and the low-frequency subband LL2; represents the pixel value of the pixel in the i-th row and the j-th column of the low-frequency subband LL1; and respectively represent the mean value and the standard deviation of the low-frequency subband LL1; , , represent the mean value adjustment coefficient, the standard deviation adjustment coefficient and the global adjustment coefficient of the low-frequency subband LL1 in sequence; represents the adaptive enhancement coefficient of the low-frequency subband LL1; represents the default enhancement coefficient of the low-frequency subband LL1; represents the total enhancement coefficient of the low-frequency subband LL1; represents the low-frequency adaptive linear enhancement on the low-frequency subband LL1, and the enhanced low-frequency subband obtained after the enhancement; represents the pixel value of the pixel in the i-th row and the j-th column of the low-frequency subband LL2; and respectively represent the mean value and the standard deviation of the low-frequency subband LL2; , , represent the mean value adjustment coefficient, the standard deviation adjustment coefficient and the global adjustment coefficient of the low-frequency subband LL2 in sequence; represents the adaptive enhancement coefficient of the low-frequency subband LL2; represents the default enhancement coefficient of the low-frequency subband LL2; represents the total enhancement coefficient of the low-frequency subband LL2; represents the low-frequency adaptive linear enhancement on the low-frequency subband LL2, and the enhanced low-frequency subband obtained after the enhancement.

[0055] Preferably, S4 is implemented based on the following formula:

[0056] ;

[0057] wherein, represents the reconstructed terahertz near-field radar image; * represents convolution operation.

[0058] Preferably, S5 is implemented based on the following formula:

[0059] ;

[0060] wherein, represents the enhanced terahertz near-field radar image.

[0061] Preferably, the above-mentioned enhancement method of terahertz near-field radar image further comprises the following steps:

[0062] S6: performing local contrast optimization on the enhanced terahertz near-field radar image by using a limited-contrast histogram equalization algorithm to obtain a final enhanced terahertz near-field radar image.

[0063] Preferably, S6 specifically comprises the following steps:

[0064] S61: dividing the enhanced terahertz near-field radar image into m x n local regions; wherein the size of each local region is ; m and n are positive integers;

[0065] S62: calculating the gray level histogram of the s-th local region ; wherein k represents the gray level; ; ;

[0066] S63: performing threshold clipping on the gray level histogram to obtain a clipped gray level histogram ; wherein represents the smaller value between and 2;

[0067] S64: calculating the cumulative distribution function of the s-th local region ;

[0068] S65: taking the pixel value into k in to obtain an enhanced pixel ; wherein represents the pixel value of the e-th pixel in ; the e-th pixel in is located in the s-th local region; ; the enhanced pixel represents the pixel value of the e-th pixel in the s-th local region in the final enhanced terahertz near-field radar image.

[0069] According to the above technical solution, compared with the prior art, the present disclosure provides an enhancement method of terahertz near-field radar image, which can enhance the contrast of the terahertz near-field radar image to be enhanced, highlight the edge features of the terahertz near-field radar image to be enhanced, and enrich the detail information of the terahertz near-field radar image to be enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0070] In order to make the technical solutions of the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description only aim to explain the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative effort based on the provided drawings.

[0071] Figure 1 A flowchart of a method for enhancing a terahertz near-field radar image according to an embodiment of the present application is shown in FIG. 1.

[0072] Figure 2 A terahertz near-field radar image to be enhanced according to an embodiment of the present application is shown in FIG. 2, where (a) is a first terahertz near-field radar image to be enhanced, and (b) is a second terahertz near-field radar image to be enhanced.

[0073] Figure 3 A terahertz near-field radar image enhanced according to an embodiment of the present application is shown in FIG. 3, where (a) is a first terahertz near-field radar image enhanced, and (b) is a second terahertz near-field radar image enhanced. DETAILED DESCRIPTION

[0074] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort fall within the scope of the present application.

[0075] As shown in FIG. 1, a method for enhancing a terahertz near-field radar image according to an embodiment of the present application includes the following steps: Figure 1

[0076] S1: data conversion and normalization are performed on a terahertz near-field radar image to be enhanced to obtain a normalized terahertz near-field radar image.

[0077] In an embodiment, the data conversion in S1 converts the pixel values of all pixels of the terahertz near-field radar image to be enhanced from 8-bit unsigned integers to 32-bit floating-point numbers.

[0078] The normalization in S1 linearly maps the pixel values of all pixels of the terahertz near-field radar image to be enhanced after data conversion from the interval [0, 255] to the interval [0, 1].

[0079] ​In an embodiment, the terahertz near-field radar image to be enhanced is acquired by a terahertz near-field radar imaging system using active planar array imaging, the imaging frequency range of the terahertz near-field radar imaging system is 5-10 Thz, and the pixel size of the terahertz near-field radar image to be enhanced is 640*480.

[0080] It can be understood that converting the pixel value of the 8-bit unsigned integer into a 32-bit floating point number can avoid the precision loss caused by subsequent division.

[0081] In an embodiment, the normalization adopts the following formula: ; The normalized terahertz near-field radar image is represented as D', and the interval is [0, 1]. The normalized terahertz near-field radar image is represented as D', and the interval is [0, 1].

[0082] S2: performing stationary wavelet transform decomposition on the normalized terahertz near-field radar image to obtain a plurality of low-frequency subbands and a plurality of high-frequency subbands;

[0083] In an embodiment, S2 specifically includes the following steps:

[0084] S21: performing first-level decomposition on the normalized terahertz near-field radar image using the low-pass filter and the high-pass filter of the Haar filter bank to obtain a low-frequency subband LL1, a horizontal high-frequency subband LH1, a vertical high-frequency subband HL1, and a diagonal high-frequency subband HH1;

[0085] S22: performing second-level decomposition on the low-frequency subband LL1 using the low-pass filter and the high-pass filter of the Haar filter bank to obtain a low-frequency subband LL2, a horizontal high-frequency subband LH2, a vertical high-frequency subband HL2, and a diagonal high-frequency subband HH2.

[0086] In an embodiment, the calculation formulas of the low-frequency subband LL1, the horizontal high-frequency subband LH1, the vertical high-frequency subband HL1, the diagonal high-frequency subband HH1, the low-frequency subband LL2, the horizontal high-frequency subband LH2, the vertical high-frequency subband HL2, and the diagonal high-frequency subband HH2 are as follows:

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] wherein, denotes the normalized terahertz near-field radar image; and denotes the low-pass filter and the high-pass filter of the Haar filter bank, respectively; denotes a convolution operation; T denotes a transpose.

[0096] It can be understood that the low-frequency component is used to preserve luminance information, and the high-frequency component is used to capture different directional details.

[0097] In an embodiment, ; .

[0098] S3: performing high-frequency multi-directional filtering on the high-frequency subbands by using filters of different directions to obtain a plurality of enhanced high-frequency subbands;

[0099] In an embodiment, the high-frequency multi-directional filtering on the high-frequency subbands by using filters of different directions to obtain a plurality of enhanced high-frequency subbands is implemented based on the following formula:

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] wherein, denotes a convolution operation; K r denotes the rthdirectional filter, r = 1, 2,..., 8, K1denotes a horizontal directional filter, K2denotes a vertical directional filter, K3denotes a 45° diagonal directional filter, K4denotes a 135° diagonal directional filter, K5denotes a horizontal edge enhancement filter, K6denotes a vertical edge enhancement filter, K7denotes a Laplacian filter, and K8denotes a sharpening filter; denotes performing high-frequency multi-directional filtering on the horizontal high-frequency subband LH1to obtain an enhanced horizontal high-frequency subband; represents a high-frequency multi-directional filtering on the vertical high-frequency subband HL1, and an enhanced vertical high-frequency subband obtained thereby; represents a high-frequency multi-directional filtering on the diagonal high-frequency subband HH1, and an enhanced diagonal high-frequency subband obtained thereby; represents a high-frequency multi-directional filtering on the horizontal high-frequency subband LH2, and an enhanced horizontal high-frequency subband obtained thereby; represents a high-frequency multi-directional filtering on the vertical high-frequency subband HL2, and an enhanced vertical high-frequency subband obtained thereby; represents a high-frequency multi-directional filtering on the diagonal high-frequency subband HH2, and an enhanced diagonal high-frequency subband obtained thereby.

[0107] In an embodiment, .

[0108] It should be noted that:

[0109] The horizontal direction filter K1 is used to calculate the gradient of the vertical direction of the image, and to strengthen the brightness change in the horizontal direction;

[0110] The vertical direction filter K2 is used to calculate the gradient of the horizontal direction of the image, and to strengthen the brightness change in the vertical direction;

[0111] The 45° diagonal direction filter K3 is used to detect the edge in the 135° direction, i.e. the brightness change from the upper left to the lower right;

[0112] The 135° diagonal direction filter K4 is used to detect the edge in the 45° direction, i.e. the brightness change from the upper right to the lower left;

[0113] The horizontal edge enhancement filter K5 enhances the horizontal edge through high weight of the center row;

[0114] The vertical edge enhancement filter K6 enhances the vertical edge through high weight of the center column;

[0115] The Laplace filter K7 enhances the edge in all directions;

[0116] The sharpening filter K8 is used for image sharpening.

[0117] Low-frequency adaptive linear enhancement is performed on the low-frequency subbands by using the mean value and the standard deviation of the low-frequency subbands, and a plurality of enhanced low-frequency subbands are obtained;

[0118] ​​​​​​​In an embodiment, low-frequency adaptive linear enhancement is performed on the low-frequency subband using the mean and standard deviation of the low-frequency subband to obtain a plurality of enhanced low-frequency subbands, which is implemented based on the following formula:

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] ;

[0132] ;

[0133] ;

[0134] ;

[0135] wherein M represents the number of rows of the low-frequency subband LL1 and the low-frequency subband LL2; N represents the number of columns of the low-frequency subband LL1 and the low-frequency subband LL2; represents the pixel value of the pixel in the i-th row and the j-th column of the low-frequency subband LL1; and respectively represent the mean and the standard deviation of the low-frequency subband LL1; , , respectively represent the mean adjustment coefficient, the standard deviation adjustment coefficient and the global adjustment coefficient of the low-frequency subband LL1; represents the adaptive enhancement coefficient of the low-frequency subband LL1; a default enhancement coefficient of the low-frequency subband LL1; a total enhancement coefficient of the low-frequency subband LL1; a low-frequency self-adaptive linearly enhanced low-frequency subband obtained by performing low-frequency self-adaptive linear enhancement on the low-frequency subband LL1; a pixel value of an i-th row and j-th column pixel in the low-frequency subband LL2; and respectively represent a mean value and a standard deviation of the low-frequency subband LL2; 、 、 respectively represent a mean value adjustment coefficient, a standard deviation adjustment coefficient and a global adjustment coefficient of the low-frequency subband LL2; an adaptive enhancement coefficient of the low-frequency subband LL2; a default enhancement coefficient of the low-frequency subband LL2; a total enhancement coefficient of the low-frequency subband LL2; a low-frequency self-adaptive linearly enhanced low-frequency subband obtained by performing low-frequency self-adaptive linear enhancement on the low-frequency subband LL2.

[0136] It can be understood that: and reflect the overall brightness of the image; and measure the low-frequency contrast;

[0137] When the image is dark or bright, the application sets a threshold-based adjustment mechanism, when ( ) is less than a preset threshold 0.5, positive intervention is taken to improve the mean value; when ( ) is greater than the preset threshold 0.5, negative intervention is applied to reduce the mean value; similarly, based on the set threshold 0.2, the standard deviation ( ) is adjusted: the standard deviation ( ) adjusts the global contrast of the image, and the threshold value 0.15 detects the extremely low threshold scene, and the coefficient 1.2 provides a gentle adjustment:

[0138] The application sets ( ) coefficient to 0.7, that is, the adaptive dominant accounts for 70%, and preferentially relies on data-driven dynamic adjustment; set ( ) coefficient to 0.3, that is, artificial intervention accounts for 30%, to prevent excessive correction in extreme cases and improve robustness.

[0139] S4: performing inverse stationary wavelet transform on the plurality of enhanced high-frequency subbands and the plurality of enhanced low-frequency subbands to obtain a reconstructed terahertz near-field radar image;

[0140] In an embodiment, S4 is implemented based on the following formula:

[0141] ;

[0142] wherein, denotes the reconstructed terahertz near-field radar image; and denotes a convolution operation.

[0143] S5: performing linear transformation on the reconstructed terahertz near-field radar image to obtain an enhanced terahertz near-field radar image.

[0144] In an embodiment, S5 is implemented based on the following formula:

[0145] ;

[0146] wherein, denotes the enhanced terahertz near-field radar image.

[0147] In an embodiment, the enhancement method of the terahertz near-field radar image further comprises the following steps:

[0148] S6: performing local contrast optimization (enhancing local contrast and improving dark / bright area details) on the enhanced terahertz near-field radar image by using a limited contrast histogram equalization algorithm to obtain a final enhanced terahertz near-field radar image.

[0149] In an embodiment, S6 specifically comprises the following steps:

[0150] S61: dividing the enhanced terahertz near-field radar image into m x n local regions; wherein the size of each local region is ; m and n are positive integers;

[0151] S62: calculating the gray level histogram of the s-th local region ; wherein k denotes a gray level; ; ;

[0152] S63: performing threshold clipping on the gray level histogram to obtain a clipped gray level histogram ; wherein denotes taking the smaller value between and 2;

[0153] S64: calculating the cumulative distribution function of the s-th local region ;

[0154] S65: put the pixel value k in the equation , obtain the enhanced pixel ; wherein, represents the pixel value of the e-th pixel in ; the e-th pixel in is located in the s-th local region; ; the enhanced pixel represents the pixel value of the e-th pixel of the s-th local region in the final enhanced terahertz near-field radar image.

[0155] Finally, from Figure 2 and Figure 3 , it can be seen that the present application can enhance the contrast of the terahertz near-field radar image to be enhanced, highlight the edge features of the terahertz near-field radar image to be enhanced, and enrich the detail information of the terahertz near-field radar image to be enhanced.

[0156] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0157] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.​

Claims

1. A method for enhancing terahertz near-field radar images, characterized in that, Includes the following steps: S1: Perform data conversion and normalization on the enhanced terahertz near-field radar image to obtain a normalized terahertz near-field radar image; S2: Perform stationary wavelet transform decomposition on the normalized terahertz near-field radar image to obtain several low-frequency sub-bands and several high-frequency sub-bands. S2 specifically includes the following steps: S21: The normalized terahertz near-field radar image is decomposed into a first-level decomposition using the low-pass and high-pass filters of the Haar filter bank to obtain the low-frequency subband LL1, the horizontal high-frequency subband LH1, the vertical high-frequency subband HL1, and the diagonal high-frequency subband HH1. S22: The low-frequency subband LL1 is decomposed into low-frequency subband LL2, horizontal high-frequency subband LH2, vertical high-frequency subband HL2 and diagonal high-frequency subband HH2 by using the low-pass and high-pass filters of the Haar filter bank. The calculation formulas for the low-frequency subband LL1, the horizontal high-frequency subband LH1, the vertical high-frequency subband HL1, the diagonal high-frequency subband HH1, the low-frequency subband LL2, the horizontal high-frequency subband LH2, the vertical high-frequency subband HL2, and the diagonal high-frequency subband HH2 are as follows: ； ； ； ； ； ； ； ； in, This represents the normalized terahertz near-field radar image; and The following characters represent the low-pass and high-pass filters of the Haar filter bank, respectively; * indicates convolution operation; T indicates transpose. S3: High-frequency subbands are filtered in multiple directions using filters of different directions to obtain several enhanced high-frequency subbands; High-frequency subbands are subjected to multi-directional high-frequency filtering using filters in different directions to obtain several enhanced high-frequency subbands, based on the following formula: ； ； ； ； ； ； Where * represents the convolution operation; K r Let K1 represent the r-th directional filter, where r = 1, 2, ..., 8. K1 represents the horizontal directional filter, K2 represents the vertical directional filter, K3 represents the 45° diagonal directional filter, K4 represents the 135° diagonal directional filter, K5 represents the horizontal edge enhancement filter, K6 represents the vertical edge enhancement filter, K7 represents the Laplace filter, and K8 represents the sharpening filter. This indicates that the enhanced horizontal high-frequency subband LH1 is obtained by performing high-frequency multi-directional filtering on the horizontal high-frequency subband LH1. This indicates the enhanced vertical high-frequency subband obtained by performing high-frequency multi-directional filtering on the vertical high-frequency subband HL1. This indicates that the enhanced diagonal high-frequency subband HH1 is obtained by performing high-frequency multi-directional filtering on the diagonal high-frequency subband HH1. This indicates that the enhanced horizontal high-frequency subband LH2 is obtained by performing high-frequency multi-directional filtering on the horizontal high-frequency subband LH2. This indicates the enhanced vertical high-frequency subband obtained by performing high-frequency multi-directional filtering on the vertical high-frequency subband HL2. This indicates that the enhanced diagonal high-frequency subband HH2 is obtained by performing high-frequency multi-directional filtering on the diagonal high-frequency subband HH2. The mean and standard deviation of the low-frequency subband are used to perform low-frequency adaptive linear enhancement of the low-frequency subband, resulting in several enhanced low-frequency subbands. Low-frequency subbands are enhanced using adaptive linear enhancement based on the mean and standard deviation of the low-frequency subbands, resulting in several enhanced low-frequency subbands. This is achieved using the following formula: ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； Where M represents the number of rows of the low-frequency subband LL1 and the low-frequency subband LL2; N represents the number of columns of the low-frequency subband LL1 and the low-frequency subband LL2; This represents the pixel value of the pixel in the i-th row and j-th column of the low-frequency subband LL1; and These represent the mean and standard deviation of the low-frequency subband LL1, respectively. , , These represent the mean adjustment coefficient, standard deviation adjustment coefficient, and global adjustment coefficient of the low-frequency subband LL1, respectively. This represents the adaptive enhancement coefficient of the low-frequency subband LL1; This represents the default enhancement factor of the low-frequency subband LL1; This represents the total enhancement factor of the low-frequency subband LL1; This indicates that the enhanced low-frequency subband LL1 is obtained by performing low-frequency adaptive linear enhancement. This represents the pixel value of the pixel in the i-th row and j-th column of the low-frequency subband LL2; and These represent the mean and standard deviation of the low-frequency subband LL2, respectively. , , These represent the mean adjustment coefficient, standard deviation adjustment coefficient, and global adjustment coefficient of the low-frequency subband LL2, respectively. This represents the adaptive enhancement coefficient of the low-frequency subband LL2; This represents the default enhancement factor of the low-frequency subband LL2; This represents the total enhancement factor of the low-frequency subband LL2; This indicates the enhanced low-frequency subband obtained by performing low-frequency adaptive linear enhancement on the low-frequency subband LL2; S4: Perform inverse stationary wavelet transform on the several enhanced high-frequency subbands and the several enhanced low-frequency subbands to obtain the reconstructed terahertz near-field radar image; S5: Perform a linear transformation on the reconstructed terahertz near-field radar image to obtain an enhanced terahertz near-field radar image.

2. The method for enhancing terahertz near-field radar images according to claim 1, characterized in that: The data conversion in S1 converts the pixel values ​​of all pixels in the terahertz near-field radar image to be enhanced from 8-bit unsigned integers to 32-bit floating-point numbers; The normalization in S1 linearly maps the pixel values ​​of all pixels in the enhanced terahertz near-field radar image from the interval [0, 255] to the interval [0, 1].

3. The method for enhancing terahertz near-field radar images according to claim 1, characterized in that, S4 is implemented based on the following formula: ； in, The image represents the reconstructed terahertz near-field radar image; * indicates a convolution operation.

4. The method for enhancing terahertz near-field radar images according to claim 3, characterized in that, S5 is implemented based on the following formula: ； in, This refers to the enhanced terahertz near-field radar image.

5. The method for enhancing terahertz near-field radar images according to claim 4, characterized in that, It also includes the following steps: S6: The enhanced terahertz near-field radar image is locally contrast optimized using a limited contrast histogram equalization algorithm to obtain the final enhanced terahertz near-field radar image.

6. The method for enhancing terahertz near-field radar images according to claim 5, characterized in that, S6 specifically includes the following steps; S61: Divide the enhanced terahertz near-field radar image into m×n local regions; wherein, the size of each local region is... m and n are positive integers; S62: Calculate the gray-level histogram of the s-th local region. Where k represents the gray level; ; ; S63: Gray-level histogram Threshold cropping is performed to obtain the cropped grayscale histogram. ;in, Indicates taking The smaller value between and 2; S64: Calculate the cumulative distribution function of the s-th local region. ; S65: Set pixel value Substitute In k, obtain enhanced pixels ;in, express The pixel value of the e-th pixel; The e-th pixel is located within the s-th local region; Enhanced pixels This represents the pixel value of the e-th pixel in the s-th local region of the final enhanced terahertz near-field radar image.

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