Remote sensing image enhancement method and system based on homomorphic filtering terrain correction

Abstract

The invention relates to the technical field of remote sensing image enhancement, and discloses a remote sensing image enhancement method and system based on homomorphic filtering terrain correction, and the method comprises the steps: obtaining a to-be-enhanced remote sensing image, converting the to-be-enhanced remote sensing image from an RGB color space into an HSV color space, and extracting a brightness component; homomorphic filtering processing is carried out on the brightness component based on a high-pass filter, the high-frequency component is enhanced, and the low-frequency component is suppressed. And combining the processed brightness component with saturation and hue, and inverting back to an RGB color space, thereby completing image enhancement. Then, obtaining gray level distribution of the enhanced image, and calculating the image information entropy according to the gray level distribution of the enhanced image; and further calculating the contrast of the image through the gray value of each pixel. And calculating an enhancement score of the image based on the information entropy and the contrast ratio, comparing the enhancement score with a preset score, judging whether image enhancement is completed or not, and if the preset score is not reached, performing homomorphic filtering processing again until a standard is met. According to the invention, the image quality of the remote sensing image can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image enhancement, and in particular to a remote sensing image enhancement method and system based on homomorphic filtering terrain correction. Background Art

[0002] The quality of remote sensing images directly affects the accuracy of image analysis and interpretation, so image enhancement has important application value in remote sensing image processing. Remote sensing images are often affected by imaging conditions, sensor characteristics, atmosphere and terrain, resulting in problems such as noise, low contrast and loss of details. Traditional image enhancement methods such as histogram equalization and contrast stretching can improve the overall quality of the image, but they are often unable to effectively handle detail enhancement in different image regions, and are prone to introduce artifacts in high-noise environments, affecting the accuracy of the image.

[0003] Homomorphic filtering is a method that uses frequency domain filtering to enhance specific frequency components of an image. It is widely used to enhance the brightness and contrast of an image. By separating the low-frequency and high-frequency components of an image, homomorphic filtering can enhance the detail information of the image. It is especially suitable for remote sensing images with uneven illumination or low contrast. However, traditional homomorphic filtering methods often rely on manually set filtering parameters, and may result in over-enhancement or under-enhancement of the image during the processing process, lacking automation and accuracy.

[0004] Therefore, there is an urgent need to invent a remote sensing image enhancement technology to solve the problem that when remote sensing images are enhanced by homomorphic filtering in the prior art, the influence of terrain cannot be effectively eliminated, resulting in low image quality. Summary of the invention

[0005] In view of this, the present invention proposes a remote sensing image enhancement method and system based on homomorphic filtering terrain correction, aiming to solve the problem of low image quality caused by the inability to effectively eliminate the influence of terrain when enhancing remote sensing images through homomorphic filtering in the current technology.

[0006] The present invention proposes a remote sensing image enhancement method based on homomorphic filtering terrain correction, comprising:

[0007] Acquire a remote sensing image to be enhanced, convert the remote sensing image from an RGB color space to an HSV color space, and extract a brightness component in the remote sensing image;

[0008] Performing homomorphic filtering on the brightness component based on a high-pass filter, enhancing the high-frequency component in the brightness component, and reducing the low-frequency component in the brightness component;

[0009] The brightness component after homomorphic filtering is combined with saturation and hue, and inverted back to the RGB color space to perform image enhancement on the remote sensing image;

[0010] Obtaining image grayscale distribution in the remote sensing image after image enhancement, and determining information entropy of the remote sensing image after image enhancement according to the image grayscale distribution;

[0011] Acquire the grayscale value of each pixel of the remote sensing image after image enhancement, and determine the contrast of the remote sensing image after image enhancement based on each pixel grayscale value;

[0012] According to the information entropy and contrast, an enhancement score of the remote sensing image after image enhancement is determined, and a comparison is performed between the enhancement score and a preset enhancement score, wherein:

[0013] If the enhancement score is greater than or equal to the preset enhancement score, it is determined that the remote sensing image has completed image enhancement;

[0014] If the enhancement score is lower than the preset enhancement score, it is determined that the remote sensing image has not completed image enhancement, and the remote sensing image is re-processed with homomorphic filtering until the enhancement score is greater than or equal to the preset enhancement score.

[0015] Further, when homomorphic filtering is performed on the brightness component based on a high-pass filter, it includes:

[0016] A preset high frequency gain and a preset low frequency gain are preconfigured, and the high pass filter is configured according to the preset high frequency gain to perform homomorphic filtering on the brightness component, wherein the homomorphic filtering expression is as follows:

[0017]

[0018] Wherein, H(u,v) is the brightness component processed by homomorphic filtering, u is the high-frequency component, and v is the low-frequency component; R h is the preset high frequency gain, R l is the preset low-frequency gain, D(u, v) is the frequency of the current frequency point, D0 is the low-frequency cutoff frequency, and c is the preset amplitude of the gain change.

[0019] Further, after the brightness component is subjected to homomorphic filtering, the following steps are performed:

[0020] Dividing the brightness component after homomorphic filtering into regions according to a preset distance, and performing histogram equalization processing on the divided regional images;

[0021] Acquire the total number of pixels in each of the regional images and the gray level of each of the pixels, and determine the total number of gray levels in the regional images according to the total number of pixels and the gray level of each of the pixels;

[0022] Determining a grayscale value threshold of the region according to the total number of pixels and the total number of grayscale levels in the region image;

[0023] The total number of gray levels of each of the regional images after the histogram equalization process is obtained, and according to the relationship between the total number of gray levels and the gray value threshold, it is determined whether to adjust the preset distance, wherein:

[0024] When the total number of gray levels is lower than or equal to the gray value threshold, determining not to adjust the preset distance;

[0025] When the total number of gray levels is higher than the gray value threshold, it is determined to adjust the preset distance.

[0026] Furthermore, when performing histogram equalization processing on the regional image, it includes:

[0027] Performing grayscale transformation on the regional image through a cumulative distribution histogram;

[0028] Obtaining a cumulative distribution histogram of each gray level, and extracting a cumulative distribution probability of each gray level;

[0029] Substitute the cumulative distribution probability of each gray level into Formula I to obtain a preset gray range, and map the original gray value to the preset gray range to ensure that the distributed gray value covers the preset gray range, wherein Formula I is as follows:

[0030]

[0031] Among them, CDF(k) is the cumulative distribution value of gray value k, CDF min is the non-zero minimum cumulative distribution value, and S(k) is the preset grayscale distribution range.

[0032] Furthermore, when homomorphic filtering is performed on the brightness component based on a high-pass filter, it also includes:

[0033] The brightness component is multi-scale decomposed based on a wavelet transform formula to decompose the remote sensing image into a low-frequency part and a high-frequency part, wherein the wavelet transform formula is as follows:

[0034]

[0035] Among them, z a，b(t) is the representation of the low-frequency part and the high-frequency part after the remote sensing image is decomposed, a is the scaling factor, b is the translation factor, and t is the brightness component;

[0036] The low-frequency part is the overall illumination and structural information of the remote sensing image, and the high-frequency part is the texture and grayscale information of the remote sensing image.

[0037] Furthermore, obtaining the image grayscale distribution in the remote sensing image after image enhancement, and determining the information entropy of the remote sensing image after image enhancement according to the image grayscale distribution, includes:

[0038] Obtain the occurrence probability of each gray value in the remote sensing image, and substitute the occurrence probability of each gray value into Formula II to obtain the information entropy of the remote sensing image, wherein Formula II is as follows:

[0039]

[0040] Where H is the information entropy of the remote sensing image, p i is the occurrence probability of gray value i, and n is the total number of gray levels.

[0041] Furthermore, when obtaining the grayscale value of each pixel of the remote sensing image after image enhancement, and determining the contrast of the remote sensing image after image enhancement by using each pixel grayscale value, it includes:

[0042] Obtaining the width and length of the remote sensing image, the grayscale value of each pixel in the remote sensing image, and the average grayscale value of the remote sensing image;

[0043] Substitute the width and length of the remote sensing image, the grayscale value of each pixel in the remote sensing image, and the average grayscale value of the remote sensing image into Formula III to obtain the contrast of the remote sensing image, wherein Formula III is as follows:

[0044]

[0045] Among them, C is the contrast of the remote sensing image, f(i, j) is the grayscale value of the pixel, f is the average grayscale value of the remote sensing image, M is the width of the remote sensing image, and N is the length of the remote sensing image.

[0046] Further, when determining the enhancement score of the remote sensing image after image enhancement according to the information entropy and contrast, it includes:

[0047] Obtaining an information entropy difference between the information entropy and a pre-configured preset information entropy, and determining the enhancement score according to a relationship between the information entropy difference and a pre-configured first preset information entropy difference and a second preset information entropy difference:

[0048] When the information entropy difference is less than the first preset information entropy difference, and the information entropy difference is greater than zero, determining the enhancement score to be W1;

[0049] When the information entropy difference is greater than or equal to the first preset information entropy difference, and the information entropy difference is less than the second preset information entropy difference, determining the enhancement score to be W2;

[0050] When the information entropy difference is greater than or equal to the second preset information entropy difference, the enhancement score is determined to be W3;

[0051] Among them, the first preset information entropy difference is smaller than the second preset information entropy difference, and the first preset information entropy difference is greater than zero; W1<W2<W3.

[0052] Further, when the enhancement score is determined to be Wi, i=1, 2, 3, including:

[0053] Obtaining a contrast difference between the contrast and a preset contrast, and determining whether to adjust the enhancement score Wi according to a relationship between the contrast difference and a first preset contrast difference and a second preset contrast difference of a preset configuration;

[0054] When the contrast difference is less than the first preset contrast difference, it is determined that the enhancement score Wi is not adjusted;

[0055] When the contrast difference is greater than or equal to the first preset contrast difference, and the contrast difference is less than the second preset contrast difference, the adjustment coefficient is determined to be g1, and the enhancement score Wi is adjusted according to the adjustment coefficient g1;

[0056] When the contrast difference is greater than or equal to the second preset contrast difference, the adjustment coefficient is determined to be g2, and the enhancement score Wi is adjusted according to the adjustment coefficient g2;

[0057] The first preset pair difference is smaller than the second preset pair difference, and g1<g2<1.

[0058] Compared with the prior art, the beneficial effect of the present invention is that the brightness and contrast of remote sensing images can be significantly improved by a terrain correction method based on homomorphic filtering, and it is particularly suitable for remote sensing images whose details are lost due to uneven illumination or low contrast. In remote sensing image processing, uneven illumination and low contrast often affect the quality of the image, reduce the visibility of image details, and thus affect the accuracy of image analysis and interpretation. By converting the remote sensing image from the RGB color space to the HSV color space, extracting the brightness component and performing high-pass filtering on it, the high-frequency component in the image can be enhanced, thereby improving the presentation of image details. This method effectively avoids the situation of excessive or insufficient contrast enhancement in traditional image enhancement methods, and has important practical significance, especially in the application scenarios of remote sensing images. In addition, by automatically calculating the grayscale distribution, information entropy and contrast of the image, it is ensured that the effect of image enhancement meets the expected standards. Information entropy can reflect the complexity of the image, while contrast directly affects the visual effect of the image. By quantitatively evaluating these two indicators, it is possible to objectively judge whether the effect of image enhancement reaches the ideal level. The automatic comparison and evaluation during the enhancement process avoids the inconsistency of effects that may occur when adjusting parameters manually in the traditional way, and improves the automation and accuracy of image enhancement. This automatic adjustment mechanism based on standardized evaluation makes remote sensing image enhancement more intelligent and efficient, more adaptable, and able to meet the needs of different application scenarios. Finally, by setting a preset enhancement score and comparing it with the actual enhancement score, the quality control of image enhancement is ensured. If the enhancement effect does not meet expectations, the system will automatically start the iterative process of homomorphic filtering until the image enhancement effect meets the standard. This adaptive adjustment mechanism avoids the tedious manual operation and can accurately optimize for different image characteristics, improving the robustness and stability of image enhancement. This innovative measure not only improves the automation level of image processing, but also enables the enhanced image to provide higher accuracy in subsequent analysis, meeting the high-precision requirements of remote sensing data analysis, change detection, etc.

[0059] On the other hand, the present application also provides a remote sensing image enhancement system based on homomorphic filtering terrain correction, comprising:

[0060] An acquisition module, used for acquiring remote sensing images to be enhanced;

[0061] An extraction module, electrically connected to the acquisition module, for converting the remote sensing image from the RGB color space to the HSV color space, and extracting a brightness component in the remote sensing image;

[0062] A central control module is electrically connected to the extraction module, and the central control module is configured with a high-pass filter. The central control module performs homomorphic filtering on the brightness component based on the high-pass filter, and enhances the high-frequency component in the brightness component and reduces the low-frequency component in the brightness component; the central control module is also used to combine the brightness component after homomorphic filtering with saturation and hue, and invert it back to the RGB color space to perform image enhancement on the remote sensing image;

[0063] An evaluation module is electrically connected to the central control module, and is used to obtain the image grayscale distribution in the remote sensing image after image enhancement, and determine the information entropy of the remote sensing image after image enhancement based on the image grayscale distribution; the evaluation module is also used to obtain the grayscale value of each pixel of the remote sensing image after image enhancement, and determine the contrast of the remote sensing image after image enhancement based on each pixel grayscale value; the evaluation module is also used to determine the enhancement score of the remote sensing image after image enhancement based on the information entropy and contrast, and determine whether the remote sensing image has completed image enhancement based on the relationship between the enhancement score and a preset enhancement score.

[0064] It can be understood that the remote sensing image enhancement method and system based on homomorphic filtering terrain correction in the above embodiments of the present invention have the same beneficial effects and will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0066] Figure 1 A flowchart of a remote sensing image enhancement method based on homomorphic filtering terrain correction provided by an embodiment of the present invention;

[0067] Figure 2 This is a rendering of the effect of converting a remote sensing image from RGB color space to HSV space in an embodiment of the present invention;

[0068] Figure 3 This is a diagram of terrain enhancement effect of homomorphic filtering remote sensing image based on wavelet transform in an embodiment of the present invention;

[0069] Figure 4 A functional block diagram of a remote sensing image enhancement system based on homomorphic filtering terrain correction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0071] like Figure 1-Figure 3 In some embodiments of the present application, this embodiment provides a remote sensing image enhancement method based on homomorphic filtering terrain correction, including:

[0072] Step S100: Acquire a remote sensing image to be enhanced, convert the remote sensing image from an RGB color space to an HSV color space, and extract a brightness component in the remote sensing image.

[0073] It can be understood that by converting the remote sensing image from RGB color space to HSV color space, the brightness component (V component) in HSV space is used to represent the brightness information of the image. RGB color space is mainly used to represent color, while HSV color space is more suitable for processing image features related to brightness, saturation and hue. The converted brightness component (V component) can independently reflect the brightness characteristics of the image, which is convenient for subsequent image enhancement processing. By extracting the brightness component, the brightness and color information of the image can be effectively separated.

[0074] Specifically, when homomorphic filtering is performed on the brightness component based on a high-pass filter, it also includes: multi-scale decomposition of the brightness component based on a wavelet transform formula to decompose the remote sensing image into a low-frequency part and a high-frequency part, wherein the wavelet transform formula is as follows: Among them, z a，b (t) is the representation of the low-frequency part and the high-frequency part after the remote sensing image is decomposed, a is the scaling factor, b is the translation factor, and t is the brightness component. Among them, the low-frequency part is the overall illumination and structural information of the remote sensing image, and the high-frequency part is the texture and grayscale information of the remote sensing image.

[0075] It can be understood that based on the wavelet transform formula, by adjusting the scaling factor (a) and the translation factor (b), the image can be analyzed at different scales and positions. Wavelet transform can capture the detailed changes of the image at different resolutions, has strong adaptability, and can effectively process the separation of different frequency components in the image. Specifically, the low-frequency part represents the overall illumination information and structural information of the remote sensing image, including the macroscopic features of the image, such as large-scale illumination changes, the overall structure and shape of the image, etc. The high-frequency part contains the detailed information in the image, such as texture, edge and grayscale changes, reflecting the microscopic features of the image. By decomposing the brightness component of the remote sensing image into low-frequency and high-frequency parts, the system can perform different enhancement processing on different frequency components respectively. The processing of the low-frequency part is usually aimed at enhancing the illumination and overall structure of the image, improving the uniformity and clarity of the image; while the processing of the high-frequency part can focus on enhancing the texture and details of the image, improving the sharpness and contrast of the image. Through this separation processing, various aspects of the image can be adjusted more accurately, avoiding information loss in the overall image processing, and improving the visual effect of the image. In addition, the multi-scale characteristics of wavelet transform enable it to analyze images at different scales, which provides great advantages for processing complex remote sensing images. Remote sensing images often contain multi-level and multi-scale features, such as the macro structure of the terrain and tiny details. Wavelet transform can extract relevant information of the image at different scales, so that the image can better reflect the overall shape and local details of the object after processing. This multi-scale processing method is particularly suitable for the analysis of remote sensing images, because remote sensing images are usually affected by factors such as changes in illumination and perspective. Wavelet transform can effectively reduce the interference of these factors and ensure that important features in the image are accurately extracted. Another advantage of wavelet transform is its localization characteristics in time and frequency. Unlike the traditional Fourier transform, Fourier transform is global and cannot provide information on local features. Wavelet transform can effectively capture frequency changes in local areas, and is particularly suitable for processing images containing local texture and detail changes. In remote sensing image processing, local features such as buildings, roads, vegetation and other details often determine the actual application value of the image. Wavelet transform can accurately process these local areas and enhance their detail performance.

[0076] Step S200: Perform homomorphic filtering on the brightness component based on a high-pass filter, enhance the high-frequency component in the brightness component, and reduce the low-frequency component in the brightness component.

[0077] Specifically, when homomorphic filtering is performed on the brightness component based on the high-pass filter, it includes: pre-configuring a preset high-frequency gain and a preset low-frequency gain, and configuring the high-pass filter according to the preset high-frequency gain to perform homomorphic filtering on the brightness component, wherein the homomorphic filtering expression is as follows:

[0078] Among them, H(u,v) is the brightness component processed by homomorphic filtering, u is the high-frequency component, and v is the low-frequency component. h is the preset high frequency gain, R l is the preset low-frequency gain, D(u,v) is the frequency of the current frequency point, D0 is the low-frequency cutoff frequency, and c is the preset amplitude of the gain change.

[0079] It can be understood that by presetting the high frequency gain (R h ) and low frequency gain (R l ), and configure a high-pass filter to perform homomorphic filtering on the brightness component, which can enhance the high-frequency components (details) in the image while suppressing the low-frequency components (flat areas or background information). The core idea of ​​homomorphic filtering is to decompose the image into low-frequency and high-frequency components through frequency domain transformation, and then adjust them separately: the high-frequency part is enhanced (R h Gain), the low frequency part is attenuated (R l By setting the low-frequency cutoff frequency (D0) and the gain variation (c), the influence range and intensity of the filter can be precisely controlled, thereby achieving image optimization. This method is particularly suitable for improving the clarity of details in remote sensing images while reducing the interference of background noise.

[0080] Specifically, after homomorphic filtering is performed on the brightness component, the method includes: dividing the brightness component after homomorphic filtering into regions according to a preset distance, and performing histogram equalization on the divided regional images. Obtaining the total number of pixels in each regional image and the grayscale level of each pixel, and determining the total number of grayscale levels in the regional image based on the total number of pixels and the grayscale level of each pixel. Determining the grayscale value threshold of the region based on the total number of pixels and the total number of grayscale levels in the regional image. Obtaining the total number of grayscale levels of each regional image after histogram equalization, and determining whether to adjust the preset distance based on the relationship between the total number of grayscale levels and the grayscale value threshold, wherein: when the total number of grayscale levels is lower than or equal to the grayscale value threshold, it is determined that the preset distance is not adjusted. When the total number of grayscale levels is higher than the grayscale value threshold, it is determined that the preset distance is adjusted.

[0081] Specifically, when performing histogram equalization processing on a regional image, it includes: performing grayscale transformation on the regional image through a cumulative distribution histogram. Obtain the cumulative distribution histogram of each grayscale level, and extract the cumulative distribution probability of each grayscale level. Substitute the cumulative distribution probability of each grayscale level into Formula I to obtain a preset grayscale range, and map the original grayscale value to the preset grayscale range to ensure that the distributed grayscale value covers the preset grayscale range, where Formula I is as follows: Among them, CDF(k) is the cumulative distribution value of gray value k, CDFmin is the non-zero minimum cumulative distribution value, and S(k) is the preset grayscale distribution range.

[0082] It can be understood that after the brightness component is processed by homomorphic filtering, the image is divided into multiple small areas through regional division so that each area can be subjected to separate image enhancement processing. This method can not only equalize the brightness of the entire image, but also adjust the brightness difference of the local area, thereby enhancing the details of the image, especially under complex terrain and lighting conditions, the local features of the image may be masked. Through regional division, the image processing effect of each area can be more accurately controlled, so that the brightness difference between different areas of the image can be effectively improved. Histogram equalization calculates the cumulative distribution histogram (CDF) of each regional image and uses the histogram to perform grayscale transformation on the grayscale value of the image. The cumulative distribution histogram reflects the pixel distribution of each grayscale level in the image and is usually used to evaluate the contrast of the image. If the grayscale distribution of the image is concentrated in a small range, the contrast of the image is low and the details are difficult to identify. Through histogram equalization, these grayscale values ​​can be evenly distributed in the entire grayscale range, enhancing the contrast and detail level of the image, making the brightness and details of the image clearer and more visible. In order to ensure that the image enhancement effect meets the expectations, the grayscale transformation of each area should be mapped not only based on the cumulative distribution histogram, but also based on the preset grayscale range to avoid excessive or insufficient brightness adjustment. In addition, this method further optimizes the adaptive ability of image processing by dynamically adjusting the preset distance of regional division. When the grayscale distribution of a certain area is lower than the preset threshold, the system automatically identifies and decides whether to adjust the division distance of the area to ensure that the image processing effect in different areas is optimal. This adaptive adjustment mechanism makes the image processing process more intelligent, and can flexibly adjust the processing strategy according to the actual characteristics of the image, avoiding the shortcomings of manual intervention and static parameter setting. By adjusting the preset distance, the system can perform more refined processing on areas with uneven grayscale distribution, ensuring that the brightness distribution of the entire image is more uniform, thereby improving the quality and readability of the image. Finally, the grayscale transformation formula of the histogram equalization is obtained by utilizing the minimum value of the cumulative distribution histogram (CDF min ) and the preset grayscale distribution range (S(k)), the original grayscale value is mapped to ensure that the distributed grayscale value covers the preset grayscale range. Through this mapping, the grayscale value of the image will be more evenly distributed in the entire grayscale space, thereby greatly improving the overall contrast of the image. The CDF(k) in the formula represents the cumulative distribution value of the current grayscale level, and by comparing it with the CDF minBy comparing the grayscale values, the system can identify the minimum grayscale value and map it to the appropriate grayscale range to prevent the image from being too bright or too dark. This process not only enhances the details of the image, but also reduces the interference of noise to a certain extent, improving the effect of image processing.

[0083] Step S300: Combine the brightness component after homomorphic filtering with the saturation and hue, and invert it back to the RGB color space to enhance the remote sensing image.

[0084] It can be understood that in the homomorphic filtering process, the brightness component is processed by a high-pass filter, the low-frequency part is removed and the high-frequency part is enhanced, thereby improving the details and contrast of the image. Then, the processed brightness component is combined with the saturation and hue (i.e., color information) of the image, and converted from the HSV color space back to the RGB color space through an inverse transformation. This process enhances the visual effect of the image by reconstructing the relationship between brightness and color, especially in terms of lighting and detail presentation, thereby improving the overall quality of the remote sensing image. Through this technology, remote sensing images with low contrast or uneven lighting can be effectively improved, their visual performance can be enhanced, and the images can be clearer and more recognizable.

[0085] Step S400: Obtain the image grayscale distribution in the remote sensing image after image enhancement, and determine the information entropy of the remote sensing image after image enhancement according to the image grayscale distribution.

[0086] Specifically, obtaining the image grayscale distribution in the remote sensing image after image enhancement, and determining the information entropy of the remote sensing image after image enhancement according to the image grayscale distribution, includes: obtaining the occurrence probability of each grayscale value in the remote sensing image, and substituting the occurrence probability of each grayscale value into formula II to obtain the information entropy of the remote sensing image, wherein formula II is as follows: Among them, H is the information entropy of the remote sensing image, p i is the occurrence probability of gray value i, and n is the total number of gray levels.

[0087] It can be understood that the grayscale distribution of the remote sensing image after image enhancement is obtained by counting the frequency of occurrence of pixels at each grayscale level in the image. The probability of occurrence of each grayscale value (p i) reflects the proportion of the grayscale in the image. Then, by substituting these probabilities into the information entropy formula II, the overall information entropy of the image is calculated. Information entropy, as an important concept in information theory, is a tool to measure the complexity and information content of an image. The higher its value, the greater the amount of information in the image and the richer the details it contains. Conversely, it means that the image may be relatively monotonous or lack details. The calculation process of information entropy is actually an in-depth analysis of the distribution of image grayscale values. It can reflect the diversity and complexity of the image by considering the distribution of each grayscale level in the image. Specifically, images with higher information entropy usually show richer textures, details and higher recognizability. For example, in remote sensing images, details of land types, buildings, vegetation, etc. usually require higher contrast and detail expression. Images with higher information entropy can better present these detail information and therefore have stronger image expression. Through the evaluation of information entropy, an objective standard can be provided for image enhancement to help determine whether the effect of image enhancement reaches the expected goal. This process can not only be used for quality assessment of remote sensing images, but can also be widely used in other image processing fields, such as medical imaging and satellite images. In remote sensing image processing, we often face problems such as uneven illumination and low contrast. Information entropy, as an objective quantitative indicator, can effectively help determine whether the image has been improved and ensure the quality of the enhancement effect.

[0088] Step S500: obtaining the grayscale value of each pixel of the remote sensing image after image enhancement, and determining the contrast of the remote sensing image after image enhancement based on the grayscale value of each pixel.

[0089] Specifically, obtaining the grayscale value of each pixel of the remote sensing image after image enhancement, and determining the contrast of the remote sensing image after image enhancement by each pixel grayscale value, includes: obtaining the width and length of the remote sensing image, the grayscale value of each pixel in the remote sensing image, and the average grayscale value of the remote sensing image. Substituting the width and length of the remote sensing image, the grayscale value of each pixel in the remote sensing image, and the average grayscale value of the remote sensing image into formula III, the contrast of the remote sensing image is obtained, where formula III is as follows: Among them, C is the contrast of the remote sensing image, f(i,j) is the gray value of the pixel, is the average gray value of the remote sensing image, M is the width of the remote sensing image, and N is the length of the remote sensing image.

[0090] It can be understood that the contrast of an image is achieved by calculating the difference between the grayscale value of each pixel and the average grayscale value of the image. The grayscale value of each pixel in the image reflects the brightness information of the pixel, while the average grayscale value of the image represents the overall brightness level of the image. By combining these grayscale values ​​with the size information of the image, the contrast of the image can be quantified. Each item in Formula III represents an important factor that needs to be considered in the contrast calculation process. For example, the grayscale value f(i,j) of the pixel point reflects the brightness of each position, while the average grayscale value of the image is used as a reference value to measure the degree of deviation of each pixel from the overall brightness. M and N represent the width and length of the image, respectively. They provide the size information of the image for the calculation process, so that the calculation of the contrast can take into account the overall scale of the image. The contrast value calculated in this way not only provides a quantitative basis for image quality assessment, but also helps to optimize the image enhancement process. The higher the contrast, the richer the details of the image and the clearer the visual effect. This is particularly important in the processing of remote sensing images, because remote sensing images usually have a more complex ground object background, and low-contrast images will cause blurring or confusion of different ground object features. Therefore, enhancing image contrast can effectively improve the visual effect of the image, making the features of the ground objects more prominent and facilitating subsequent analysis and application. In addition, the result of contrast calculation can also be used as an evaluation criterion for whether image enhancement has achieved the expected effect. If the contrast value of the image is low, it may mean that the image enhancement processing is insufficient and further processing or adjustment may be required. If the contrast value is high, it means that the details in the image have been effectively enhanced and the image quality has been improved. In this process, using contrast as a criterion for image quality assessment can not only achieve more accurate image processing, but also reduce manual intervention and improve the automation and efficiency of remote sensing image enhancement processing.

[0091] Step S600: determining an enhancement score of the remote sensing image after image enhancement according to information entropy and contrast, and comparing the enhancement score with a preset enhancement score.

[0092] Specifically, when determining the enhancement score of the remote sensing image after image enhancement based on information entropy and contrast, it includes: obtaining the information entropy difference between the information entropy and the pre-configured preset information entropy, and determining the enhancement score based on the relationship between the information entropy difference and the pre-configured first preset information entropy difference and the second preset information entropy difference: when the information entropy difference is less than the first preset information entropy difference, and the information entropy difference is greater than zero, the enhancement score is determined to be W1. When the information entropy difference is greater than or equal to the first preset information entropy difference, and the information entropy difference is less than the second preset information entropy difference, the enhancement score is determined to be W2. When the information entropy difference is greater than or equal to the second preset information entropy difference, the enhancement score is determined to be W3. Among them, the first preset information entropy difference is less than the second preset information entropy difference, and the first preset information entropy difference is greater than zero. W1<W2<W3.

[0093] Specifically, when the enhanced score is determined to be Wi, i=1,2,3, including: obtaining the contrast difference between the contrast and the preset contrast, and determining whether to adjust the enhanced score Wi according to the relationship between the contrast difference and the first preset contrast difference and the second preset contrast difference of the preset configuration. When the contrast difference is less than the first preset contrast difference, it is determined that the enhanced score Wi is not adjusted. When the contrast difference is greater than or equal to the first preset contrast difference, and the contrast difference is less than the second preset contrast difference, the adjustment coefficient is determined to be g1, and the enhanced score Wi is adjusted according to the adjustment coefficient g1. When the contrast difference is greater than or equal to the second preset contrast difference, the adjustment coefficient is determined to be g2, and the enhanced score Wi is adjusted according to the adjustment coefficient g2. Among them, the first preset contrast difference is less than the second preset contrast difference, and g1<g2<1.

[0094] Specifically, when comparing the enhancement score with the preset enhancement score, it is included that: if the enhancement score is greater than or equal to the preset enhancement score, it is determined that the remote sensing image has completed image enhancement. If the enhancement score is lower than the preset enhancement score, it is determined that the remote sensing image has not completed image enhancement, and the remote sensing image is re-processed with homomorphic filtering until the enhancement score is greater than or equal to the preset enhancement score.

[0095] It can be understood that by calculating the enhancement score of the image, based on the change of information entropy, the quality of the image is quantified into three levels of W1, W2 and W3, representing different levels of image quality. This scoring mechanism ensures that the image is effectively enhanced in contrast and detail information during the enhancement process by comparing the difference in information entropy. Specifically, when the difference in information entropy is less than a preset threshold, the image is considered to be in a lower enhancement state. If the difference in information entropy is large, the image may be in a higher enhancement state, and then classified by the scores W1, W2 and W3. In addition, the role of contrast in image quality assessment cannot be ignored. By calculating the difference in image contrast and comparing it with the preset contrast difference, the effect of image enhancement is further refined. When the contrast difference falls within a certain threshold range, the enhancement score will be fine-tuned according to the adjustment coefficient g1 or g2 to ensure that the image is more vivid in visual effect and the details are fully displayed. Through this adjustment mechanism, the problem of over-enhancement or unclear effect of the image due to excessively high or low contrast can be avoided. On this basis, the image enhancement score is compared with the preset target enhancement score to finally determine the degree of completion of the image enhancement. If the enhancement score does not meet the preset standard, the image enhancement process will continue until the quality requirements are met. This iterative process ensures that image enhancement can be continuously optimized, avoiding over-enhancement or under-enhancement in one processing, and improving the accuracy of image processing. Through repeated adjustments and optimizations, high-quality remote sensing image enhancement effects are finally achieved. Through this comprehensive scoring mechanism, the quantitative relationship between information entropy and contrast is utilized, combined with multiple iterative adjustments, the contrast and detail performance of the image are significantly improved. This method not only effectively avoids the lack of manual adjustment in traditional methods, but also ensures the quality stability of image enhancement during automated processing, and adapts to the needs of remote sensing image enhancement in different scenarios.

[0096] In the above embodiment, the terrain correction method based on homomorphic filtering can significantly improve the brightness and contrast of remote sensing images, which is particularly suitable for remote sensing images with lost details due to uneven illumination or low contrast. In remote sensing image processing, uneven illumination and low contrast often affect the quality of the image, reduce the visibility of image details, and thus affect the accuracy of image analysis and interpretation. By converting the remote sensing image from the RGB color space to the HSV color space, extracting the brightness component and performing high-pass filtering on it, the high-frequency component in the image can be enhanced, thereby improving the presentation of image details. This method effectively avoids the situation of excessive or insufficient contrast enhancement in traditional image enhancement methods, especially in the application scenarios of remote sensing images, and has important practical significance. In addition, by automatically calculating the grayscale distribution, information entropy and contrast of the image, it is ensured that the effect of image enhancement meets the expected standards. Information entropy can reflect the complexity of the image, while contrast directly affects the visual effect of the image. By quantitatively evaluating these two indicators, it is possible to objectively judge whether the effect of image enhancement reaches the ideal level. Automatic contrast evaluation during the enhancement process avoids the inconsistency of effects that may occur when traditional parameters are manually adjusted, and improves the automation and accuracy of image enhancement. This automatic adjustment mechanism based on standardized evaluation makes remote sensing image enhancement more intelligent, efficient, and adaptable, and can meet the needs of different application scenarios. Finally, by setting a preset enhancement score and comparing it with the actual enhancement score, the quality control of image enhancement is ensured. If the enhancement effect does not meet expectations, the system will automatically start the iterative process of homomorphic filtering until the image enhancement effect meets the standard. This adaptive adjustment mechanism avoids the tedious manual operation and can accurately optimize different image characteristics, improving the robustness and stability of image enhancement. This innovative measure not only improves the automation level of image processing, but also enables the enhanced image to provide higher accuracy in subsequent analysis, meeting the high-precision requirements of remote sensing data analysis, change detection, etc.

[0097] In another preferred embodiment based on the above embodiment, Figure 4 As shown, this embodiment provides a remote sensing image enhancement system based on homomorphic filtering terrain correction, including: an acquisition module, an extraction module, a central control module and an evaluation module.

[0098] Specifically, the acquisition module is used to acquire the remote sensing image to be enhanced. The extraction module is electrically connected to the acquisition module, and the extraction module is used to convert the remote sensing image from the RGB color space to the HSV color space, and extract the brightness component in the remote sensing image. The central control module is electrically connected to the extraction module, and the central control module is configured with a high-pass filter. The central control module performs homomorphic filtering on the brightness component based on the high-pass filter, and enhances the high-frequency component in the brightness component and reduces the low-frequency component in the brightness component. The central control module is also used to combine the brightness component after homomorphic filtering with saturation and hue, and invert it back to the RGB color space to enhance the remote sensing image. The evaluation module is electrically connected to the central control module, and the evaluation module is used to obtain the image grayscale distribution in the remote sensing image after image enhancement, and determine the information entropy of the remote sensing image after image enhancement according to the image grayscale distribution. The evaluation module is also used to obtain the grayscale value of each pixel of the remote sensing image after image enhancement, and determine the contrast of the remote sensing image after image enhancement by each pixel grayscale value. The evaluation module is also used to determine the enhancement score of the remote sensing image after image enhancement based on information entropy and contrast, and to determine whether the remote sensing image has completed image enhancement based on the relationship between the enhancement score and a preset enhancement score.

[0099] It can be understood that the remote sensing image enhancement method and system based on homomorphic filtering terrain correction in the above embodiments of the present invention have the same beneficial effects and will not be described in detail.

[0100] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0101] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A remote sensing image enhancement method based on homomorphic filtering terrain correction, characterized in that: include: Acquire a remote sensing image to be enhanced, convert the remote sensing image from an RGB color space to an HSV color space, and extract a brightness component in the remote sensing image; Performing homomorphic filtering on the brightness component based on a high-pass filter, enhancing the high-frequency component in the brightness component, and reducing the low-frequency component in the brightness component; The brightness component after homomorphic filtering is combined with saturation and hue, and inverted back to the RGB color space to perform image enhancement on the remote sensing image; Obtaining image grayscale distribution in the remote sensing image after image enhancement, and determining information entropy of the remote sensing image after image enhancement according to the image grayscale distribution; Acquire the grayscale value of each pixel of the remote sensing image after image enhancement, and determine the contrast of the remote sensing image after image enhancement based on each pixel grayscale value; According to the information entropy and contrast, an enhancement score of the remote sensing image after image enhancement is determined, and a comparison is performed between the enhancement score and a preset enhancement score, wherein: If the enhancement score is greater than or equal to the preset enhancement score, it is determined that the remote sensing image has completed image enhancement; If the enhancement score is lower than the preset enhancement score, it is determined that the remote sensing image has not completed image enhancement, and the remote sensing image is re-processed with homomorphic filtering until the enhancement score is greater than or equal to the preset enhancement score.

2. The remote sensing image enhancement method based on homomorphic filtering terrain correction according to claim 1, characterized in that: When homomorphic filtering is performed on the brightness component based on a high-pass filter, the method includes: A preset high frequency gain and a preset low frequency gain are preconfigured, and the high pass filter is configured according to the preset high frequency gain to perform homomorphic filtering on the brightness component, wherein the homomorphic filtering expression is as follows: Wherein, H(u,v) is the brightness component processed by homomorphic filtering, u is the high-frequency component, and v is the low-frequency component; R h is the preset high frequency gain, R l is the preset low-frequency gain, D(u, v) is the frequency of the current frequency point, D0 is the low-frequency cutoff frequency, and c is the preset amplitude of the gain change.

3. The remote sensing image enhancement method based on homomorphic filtering terrain correction according to claim 2, characterized in that: After the brightness component is subjected to homomorphic filtering, the following steps are performed: Dividing the brightness component after homomorphic filtering into regions according to a preset distance, and performing histogram equalization processing on the divided regional images; Acquire the total number of pixels in each of the regional images and the gray level of each of the pixels, and determine the total number of gray levels in the regional images according to the total number of pixels and the gray level of each of the pixels; Determining a grayscale value threshold of the region according to the total number of pixels and the total number of grayscale levels in the region image; The total number of gray levels of each of the regional images after the histogram equalization process is obtained, and according to the relationship between the total number of gray levels and the gray value threshold, it is determined whether to adjust the preset distance, wherein: When the total number of gray levels is lower than or equal to the gray value threshold, determining not to adjust the preset distance; When the total number of gray levels is higher than the gray value threshold, it is determined to adjust the preset distance.

4. The remote sensing image enhancement method based on homomorphic filtering terrain correction according to claim 3, characterized in that: When performing histogram equalization processing on a regional image, it includes: Performing grayscale transformation on the regional image through a cumulative distribution histogram; Obtaining a cumulative distribution histogram of each gray level, and extracting a cumulative distribution probability of each gray level; Substitute the cumulative distribution probability of each gray level into Formula I to obtain a preset gray range, and map the original gray value to the preset gray range to ensure that the distributed gray value covers the preset gray range, wherein Formula I is as follows: Among them, CDF(k) is the cumulative distribution value of gray value k, CDF min is the non-zero minimum cumulative distribution value, and S(k) is the preset grayscale distribution range.

5. The remote sensing image enhancement method based on homomorphic filtering terrain correction according to claim 4, characterized in that: When homomorphic filtering is performed on the brightness component based on a high-pass filter, the method further includes: The brightness component is multi-scale decomposed based on a wavelet transform formula to decompose the remote sensing image into a low-frequency part and a high-frequency part, wherein the wavelet transform formula is as follows: Among them, Z a，b (t) is the representation of the low-frequency part and the high-frequency part after the remote sensing image is decomposed, a is the scaling factor, b is the translation factor, and t is the brightness component; The low-frequency part is the overall illumination and structural information of the remote sensing image, and the high-frequency part is the texture and grayscale information of the remote sensing image.

6. The remote sensing image enhancement method based on homomorphic filtering terrain correction according to claim 1, characterized in that: The method of obtaining the image grayscale distribution in the remote sensing image after image enhancement and determining the information entropy of the remote sensing image after image enhancement according to the image grayscale distribution comprises: Obtain the occurrence probability of each gray value in the remote sensing image, and substitute the occurrence probability of each gray value into Formula II to obtain the information entropy of the remote sensing image, wherein Formula II is as follows: Where H is the information entropy of the remote sensing image, p i is the occurrence probability of gray value i, and n is the total number of gray levels.

7. The remote sensing image enhancement method based on homomorphic filtering terrain correction according to claim 1, characterized in that: Obtaining the grayscale value of each pixel of the remote sensing image after image enhancement, and determining the contrast of the remote sensing image after image enhancement based on each pixel grayscale value, including: Obtaining the width and length of the remote sensing image, the grayscale value of each pixel in the remote sensing image, and the average grayscale value of the remote sensing image; Substitute the width and length of the remote sensing image, the grayscale value of each pixel in the remote sensing image, and the average grayscale value of the remote sensing image into Formula III to obtain the contrast of the remote sensing image, wherein Formula III is as follows: Among them, C is the contrast of the remote sensing image, f(i, j) is the grayscale value of the pixel, f is the average grayscale value of the remote sensing image, M is the width of the remote sensing image, and N is the length of the remote sensing image.

8. The remote sensing image enhancement method based on homomorphic filtering terrain correction according to claim 1, characterized in that: Determining the enhancement score of the remote sensing image after image enhancement according to the information entropy and the contrast includes: Obtaining an information entropy difference between the information entropy and a pre-configured preset information entropy, and determining the enhancement score according to a relationship between the information entropy difference and a pre-configured first preset information entropy difference and a second preset information entropy difference: When the information entropy difference is less than the first preset information entropy difference, and the information entropy difference is greater than zero, determining the enhancement score to be W1; When the information entropy difference is greater than or equal to the first preset information entropy difference, and the information entropy difference is less than the second preset information entropy difference, the enhancement score is determined to be W2; When the information entropy difference is greater than or equal to the second preset information entropy difference, the enhancement score is determined to be W3; Among them, the first preset information entropy difference is smaller than the second preset information entropy difference, and the first preset information entropy difference is greater than zero; W1<W2<W3.

9. The remote sensing image enhancement method based on homomorphic filtering terrain correction according to claim 8, characterized in that: When the enhancement score is determined to be Wi, i=1, 2, 3, including: Obtaining a contrast difference between the contrast and a preset contrast, and determining whether to adjust the enhancement score Wi according to a relationship between the contrast difference and a first preset contrast difference and a second preset contrast difference of a preset configuration; When the contrast difference is less than the first preset contrast difference, it is determined that the enhancement score Wi is not adjusted; When the contrast difference is greater than or equal to the first preset contrast difference, and the contrast difference is less than the second preset contrast difference, the adjustment coefficient is determined to be g1, and the enhancement score Wi is adjusted according to the adjustment coefficient g1; When the contrast difference is greater than or equal to the second preset contrast difference, the adjustment coefficient is determined to be g2, and the enhancement score Wi is adjusted according to the adjustment coefficient g2; The first preset pair difference is smaller than the second preset pair difference, and g1<g2<1.

10. A remote sensing image enhancement system based on homomorphic filtering terrain correction, using a remote sensing image enhancement method based on homomorphic filtering terrain correction as claimed in any one of claims 1 to 9, characterized in that: include: An acquisition module, used for acquiring remote sensing images to be enhanced; An extraction module, electrically connected to the acquisition module, for converting the remote sensing image from the RGB color space to the HSV color space, and extracting a brightness component in the remote sensing image; A central control module is electrically connected to the extraction module, and the central control module is configured with a high-pass filter. The central control module performs homomorphic filtering on the brightness component based on the high-pass filter, and enhances the high-frequency component in the brightness component and reduces the low-frequency component in the brightness component; the central control module is also used to combine the brightness component after homomorphic filtering with saturation and hue, and invert it back to the RGB color space to perform image enhancement on the remote sensing image; An evaluation module is electrically connected to the central control module, and is used to obtain the image grayscale distribution in the remote sensing image after image enhancement, and determine the information entropy of the remote sensing image after image enhancement based on the image grayscale distribution; the evaluation module is also used to obtain the grayscale value of each pixel of the remote sensing image after image enhancement, and determine the contrast of the remote sensing image after image enhancement based on each pixel grayscale value; the evaluation module is also used to determine the enhancement score of the remote sensing image after image enhancement based on the information entropy and contrast, and determine whether the remote sensing image has completed image enhancement based on the relationship between the enhancement score and a preset enhancement score.

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