SAR image quantization and enhancement method based on histogram statistics
By using a histogram-based statistical method, the problems of detail loss and insufficient enhancement in SAR image quantization and enhancement are solved, achieving adaptive quantization and nonlinear enhancement, thereby improving image quality and applicability.
Patent Information
- Application Number
- CN202511376229.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-25
- Publication Date
- 2026-01-27
AI Technical Summary
Existing SAR image quantization methods tend to result in the loss of low-grayscale details or oversaturation in high-grayscale areas. Enhancement methods have a gradual effect, insufficient contrast improvement, and poor versatility.
A histogram-based statistical method is adopted to obtain the extreme values of the amplitude diagram through modulo processing. Parameters are set for normalization and downsampling, and combined with S-curve enhancement, adaptive quantization and nonlinear enhancement are achieved.
It achieves adaptive quantization range adjustment, improves quantization accuracy and enhancement effect, suppresses noise, preserves details of weakly scattering targets, and adapts to SAR image processing in different scenarios.
Smart Images
Figure CN121414591A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of synthetic aperture radar image processing technology, and discloses a SAR image quantization and enhancement method based on histogram statistics. Background Technology
[0002] Synthetic Aperture Radar (SAR), as an active microwave remote sensing device, has the advantages of all-day, all-weather, and high-resolution imaging. However, its imaging process is affected by speckle noise, resulting in uneven grayscale distribution and wide dynamic range, which poses challenges to subsequent image quantization and enhancement.
[0003] Existing SAR image quantization methods mostly employ fixed dynamic range cropping or linear normalization strategies: if linear quantization is performed directly based on the global maximum / minimum value of the image, it is easy to compress or even lose low grayscale details, or oversaturate high grayscale areas; if a fixed quantization interval is used, it cannot adapt to the differences in the dynamic range of SAR images under different scenarios, resulting in poor versatility.
[0004] Meanwhile, existing SAR image enhancement methods have obvious drawbacks: the large dynamic range of image amplitude makes it difficult to distinguish detailed information; histogram equalization methods can expand the gray range, but may cause image brightness distortion and are not adaptable to the non-uniform gray distribution of SAR images; nonlinear enhancement methods can suppress noise, but the enhancement effect is gradual and the improvement in the distinction between strong and weak scattering targets is limited. Summary of the Invention
[0005] This invention aims to solve the technical problems of existing enhancement methods, such as easy amplification of noise, resulting in brightness distortion, insufficient contrast improvement, low overall processing efficiency, and poor versatility, and provides a SAR image quantization and enhancement method based on histogram statistics.
[0006] To achieve the above-mentioned technical effects, the technical solution adopted by this invention is: a SAR image quantization and enhancement method based on histogram statistics, comprising: The SAR image is processed by taking its modulus to obtain the SAR amplitude map, and the minimum and maximum values of all pixels in the SAR amplitude map are obtained. Set the parameters required for SAR image quantization and enhancement, including histogram statistical order, image brightness adjustment factor, and downsampling number; Based on the histogram statistical order, the minimum value, and the maximum value, the SAR amplitude map is normalized to obtain a first normalized amplitude map; based on the downsampling number, sample points are extracted from the first normalized amplitude map, and the histogram results are obtained. The amplitude quantization upper limit is determined based on the histogram results and the image brightness adjustment factor. Based on the amplitude quantization upper limit, the first normalized amplitude map is subjected to amplitude limiting and second normalization processing to obtain the second normalized amplitude map. The S-curve enhancement method is used to enhance the contrast of the second normalized amplitude map, resulting in an enhanced amplitude map. The enhanced amplitude map is subjected to grayscale quantization to obtain the quantized and enhanced SAR image.
[0007] As a preferred embodiment, the minimum value expression of the SAR amplitude map is: A min =min(A), the maximum value expression of the SAR amplitude map is: A max =max(A); Where min(A) is the minimum value of all pixels in the SAR amplitude map, max(A) is the maximum value of all pixels in the SAR amplitude map, and A is all pixels in the SAR amplitude map.
[0008] In a preferred embodiment, the histogram statistical order is between 4096 and 16384, the image brightness adjustment factor is between 0.12 and 0.2, and the downsampling number is an integer between 1 and 16.
[0009] As a preferred implementation, the expression for the first normalized magnitude plot is:
[0010] Among them, A norm1 For the first normalized magnitude plot, A norm1 The amplitude variation range is [0, M-1], where M is the histogram statistical order, and A min A is the minimum value of all pixels in the SAR amplitude map. max Let A be the maximum value of all pixels in the SAR amplitude map, and let A be the total number of pixels in the SAR amplitude map.
[0011] As a preferred embodiment, the histogram statistics process is represented by the following formula: H(k)=H(k)+1,k=round(A norm1 xN) Where H(k) is the statistical frequency of the histogram at the k-th sample point, N is the statistical order of the histogram, and A norm1 This is the first normalized amplitude plot.
[0012] As a preferred implementation, the expression for the second normalized magnitude plot is:
[0013] Among them, Anorm2 For the second normalized magnitude plot, A norm2 The amplitude variation range is [0,1], A norm1 This is the first normalized amplitude map, where T is the upper limit of amplitude quantization determined by the histogram results and the image brightness adjustment factor.
[0014] As a preferred embodiment, the mapping expression of the enhanced amplitude map is obtained as follows:
[0015] Where B is the enhanced amplitude map, e is a constant, k is the contrast enhancement coefficient, and θ is the enhancement threshold, which takes the value [0,1].
[0016] As a preferred implementation, the expression for the quantized and enhanced SAR image is: C = round(B x 255) Where C is the quantized and enhanced SAR image, and B is the enhanced amplitude map.
[0017] Compared with the prior art, the beneficial effects of this invention are: I. Strong Quantization Adaptability: The quantization upper limit is determined based on histogram statistics and brightness adjustment factor. It can be adaptively adjusted according to the dynamic range and grayscale distribution of different SAR images, avoiding the loss of details or oversaturation caused by fixed quantization intervals, thus improving quantization accuracy. II. Superior Enhancement Effect: Employing S-curve nonlinear enhancement, it effectively suppresses speckle noise while improving the contrast between the target and the background, enhancing the detail retention rate of weakly scattering targets, and eliminating brightness distortion issues. Third, it has strong versatility: core parameters such as histogram order, brightness adjustment factor, and enhancement coefficient can be flexibly adjusted according to the scene, such as urban, mountainous, and marine, to adapt to SAR images with different resolutions and different scattering characteristics, and has a wide range of applications. Attached Figure Description
[0018] Figure 1 This is a logic diagram of the SAR image quantization and enhancement method based on histogram statistics of the present invention; Figure 2 This is one of the histogram result views of the present invention; Figure 3 This is a second histogram result view of the present invention; Figure 4 This is the upper limit diagram of amplitude quantization in this invention; Figure 5 This is a mapping diagram for the ratio enhancement of the present invention. Detailed Implementation
[0019] The present invention will now be described in further detail with reference to the embodiments and accompanying drawings. However, this should not be construed as limiting the scope of the above-described subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.
[0020] refer to Figure 1 Example 1: A SAR image quantization and enhancement method based on histogram statistics. This method uses SAR image amplitude extraction and dynamic range analysis, combined with histogram statistics to determine the adaptive quantization interval, and then optimizes the SAR image quality through S-curve nonlinear enhancement and grayscale quantization. The specific steps are as follows: Step 1: SAR Amplitude Map Extraction and Extreme Value Calculation The input SAR raw image is processed by taking the modulus to remove phase information interference, and a SAR amplitude map that only reflects the target scattering intensity is obtained. The SAR amplitude map is denoted as matrix A, where each element corresponds to a pixel in the image. By traversing all pixels in the SAR amplitude map matrix A, the minimum and maximum values of the SAR amplitude map are calculated. The minimum value expression for the SAR amplitude map is: A min =min(A), the maximum value expression of the SAR amplitude map is: A max =max(A); Where min(A) is the minimum value of all pixels in the SAR amplitude map, max(A) is the maximum value of all pixels in the SAR amplitude map, and A is all pixels in the SAR amplitude map.
[0021] The purpose of step one is to obtain the actual dynamic range boundary of the SAR image, providing a basis for subsequent normalization.
[0022] Step 2: Setting Processing Parameters Based on the resolution of the SAR image and the complexity of the scene, such as urban areas, mountainous areas, and oceans, three processing parameters are set: Histogram statistical order: The value ranges from 4096 to 16384, which is used to define the gray-level granularity of the histogram. The higher the order, the more finely the histogram describes the gray-level distribution, but the computational cost increases slightly. The preferred value is 8192, which balances accuracy and efficiency. Image brightness adjustment factor: The value ranges from 0.12 to 0.2, and it is used to control the tightness of the subsequent quantization upper limit. The larger the factor, the higher the quantization upper limit and the more high grayscale information is retained. It can be adjusted according to the overall brightness requirements of the image; the preferred value is 0.15. Downsampling number: An integer between 1 and 16, used as the downsampling ratio when extracting sample points from the normalized amplitude map. Downsampling can reduce the computational load of histogram statistics and avoid interference from individual noise points on the statistical results. A downsampling number of 4 means that 1 sample point is extracted every 4×4 pixels, and a value of 4 is preferred.
[0023] Step 3: First Normalization and Histogram Statistics First normalization process: Based on the histogram statistical order set in step 2, the SAR amplitude map is linearly normalized, mapping its grayscale range to [0, M-1], to obtain the first normalized amplitude map. The calculation formula is as follows:
[0024] Among them, A norm1 For the first normalized magnitude plot, A norm1 The grayscale range is [0, M-1], where M is the histogram statistical order, and A min A is the minimum value of all pixels in the SAR amplitude map. max Let A be the maximum value of all pixels in the SAR amplitude map, and let A be the total number of pixels in the SAR amplitude map.
[0025] The purpose of the first normalization process is to adapt the dynamic range of the original amplitude plot to the interval corresponding to the statistical order of the histogram, so as to ensure uniform statistical granularity.
[0026] Histogram statistics: Based on the downsampling number set in step two, sample points are extracted from the first normalized amplitude map, and the gray-level distribution of the sample points is statistically analyzed to obtain the histogram results (refer to 2 and 3). The statistical formula is as follows: H(k)=H(k)+1,k=round(A norm1 xN) Where H(k) is the count of sample points at the k-th gray level in the histogram, k ranges from [0, M-1], N is the histogram statistical order, and A norm1 This is the first normalized amplitude plot; Histogram statistics provide data support for determining the upper limit of adaptive quantization by statistically analyzing the gray-scale distribution.
[0027] Step 4: Determine the upper limit of amplitude quantization and perform the second normalization process. refer to Figure 4 Based on the histogram results obtained in step three and the image brightness adjustment factor set in step two, the upper limit of amplitude quantization is determined. First, the pixel values exceeding the amplitude quantization upper limit in the first normalized amplitude image are cropped. Then, the cropped image is normalized to [0,1] to obtain the second normalized amplitude image. The calculation formula for the second normalized amplitude image is as follows:
[0028] Among them, A norm2 For the second normalized magnitude plot, A norm2 The grayscale range is [0,1], A norm1 This is the first normalized amplitude map, where T is the upper limit of amplitude quantization determined by the histogram results and the image brightness adjustment factor.
[0029] The purpose of step four is to compress the image grayscale range to a reasonable range, laying the foundation for subsequent nonlinear enhancement, while avoiding interference from extreme values.
[0030] Step 5: Enhance the contrast of the S-curve refer to Figure 5 An S-curve is used to non-linearly enhance the second normalized amplitude map. By adjusting the grayscale stretching degree in low-grayscale and high-grayscale regions, the contrast between the target and the background is improved, resulting in the enhanced amplitude map B. The mapping relationship formula is as follows:
[0031] Where B is the enhanced amplitude map, e is a constant, e=2.718, k is the contrast enhancement coefficient with a value range of 5-20. The larger k is, the more significant the contrast enhancement. The preferred value is 10. θ is the enhancement threshold with a value of [0,1]. The preferred value is 0.5 to ensure the enhancement effect on low and medium grayscale details.
[0032] The advantage of the S-curve is that it can stretch the grayscale of low-grayscale, weakly scattering targets, moderately suppress high-grayscale, strongly scattering targets, and avoid noise amplification caused by linear enhancement, thus achieving a balance between "detail preservation, contrast enhancement, and noise suppression".
[0033] Step Six: Grayscale Quantization The enhanced amplitude image B is quantized into an 8-bit grayscale image to obtain the quantized and enhanced SAR image, as shown in the following formula: C = round(B x 255) Wherein, C is the quantized and enhanced SAR image, and B is the enhanced amplitude map.
[0034] Step six completes the conversion from the enhanced floating-point image to a standard grayscale image, meeting the requirements for subsequent storage and interpretation.
[0035] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A SAR image quantization and enhancement method based on histogram statistics, characterized in that, include: The SAR image is processed by taking its modulus to obtain the SAR amplitude map, and the minimum and maximum values of all pixels in the SAR amplitude map are obtained. Set the parameters required for SAR image quantization and enhancement, including histogram statistical order, image brightness adjustment factor, and downsampling number; Based on the histogram statistical order, the minimum value and the maximum value, the SAR amplitude map is normalized to obtain the first normalized amplitude map; Based on the downsampling number, sample points are extracted from the first normalized amplitude map, and the histogram results are obtained statistically. The amplitude quantization upper limit is determined based on the histogram results and the image brightness adjustment factor. Based on the amplitude quantization upper limit, the first normalized amplitude map is subjected to amplitude limiting and second normalization processing to obtain the second normalized amplitude map. The S-curve enhancement method is used to enhance the contrast of the second normalized amplitude map, resulting in an enhanced amplitude map. The enhanced amplitude map is subjected to grayscale quantization to obtain the quantized and enhanced SAR image.
2. The SAR image quantization and enhancement method based on histogram statistics according to claim 1, characterized in that, The minimum value expression for the SAR amplitude map is: A min =min(A), the maximum value expression of the SAR amplitude map is: A max =max(A); Where min(A) is the minimum value of all pixels in the SAR amplitude map, max(A) is the maximum value of all pixels in the SAR amplitude map, and A is all pixels in the SAR amplitude map.
3. The SAR image quantization and enhancement method based on histogram statistics according to claim 2, characterized in that, The histogram statistical order ranges from 4096 to 16384, the image brightness adjustment factor ranges from 0.12 to 0.2, and the downsampling number ranges from 1 to 16.
4. The SAR image quantization and enhancement method based on histogram statistics according to claim 3, characterized in that, The expression for the first normalized magnitude plot is: Among them, A norm1 For the first normalized magnitude plot, A norm1 The grayscale range is [0, M-1], where M is the histogram statistical order, and A min A is the minimum value of all pixels in the SAR amplitude map. max Let A be the maximum value of all pixels in the SAR amplitude map, and let A be the total number of pixels in the SAR amplitude map.
5. The SAR image quantization and enhancement method based on histogram statistics according to claim 4, characterized in that, The process of histogram statistics is represented by the following formula: H(k)=H(k)+1,k=round(A norm1 xN) Where H(k) is the sample point count of the k-th gray level in the histogram, N is the histogram statistical order, and A norm1 This is the first normalized amplitude plot.
6. The SAR image quantization and enhancement method based on histogram statistics according to claim 5, characterized in that, The expression for the second normalized magnitude plot is: Among them, A norm2 For the second normalized magnitude plot, A norm2 The grayscale range is [0,1], A norm1 This is the first normalized amplitude map, where T is the upper limit of amplitude quantization determined by the histogram results and the image brightness adjustment factor.
7. The SAR image quantization and enhancement method based on histogram statistics according to claim 6, characterized in that, The mapping expression for the enhanced amplitude map is: Where B is the enhanced amplitude map, e is a constant, k is the contrast enhancement coefficient, and θ is the enhancement threshold, which takes the value [0,1].
8. The SAR image quantization and enhancement method based on histogram statistics according to claim 7, characterized in that, The expression for the quantized and enhanced SAR image is: C = round(B x 255) Wherein, C is the quantized and enhanced SAR image, and B is the enhanced amplitude map.