An interferometric image contaminant identification method based on parasitic image interference cancellation

Abstract

The application discloses an interference image pollutant identification method based on parasitic image interference elimination, solves the problem that pollutant characterization is not obvious and is easy to be interfered by parasitic images, leading to inaccurate pollutant identification in the process of identifying pollutants in an interference image, and the method comprises the following steps: firstly, polynomial fitting is performed on the interference dimension of the interference image to correct the image signal, and correction coefficients of the interference dimension and the amplitude width dimension are calculated; then, parasitic image interference in the image is eliminated through relative radiation correction processing. Then, the image is decomposed into different frequency components by using Haar wavelet transform, a vertical edge information image is obtained, feature intensity is calculated, and a region with high feature intensity is screened out and identified as a pollutant. Finally, the position of the pollutant is mapped back to the original interference image through coordinate restoration. The application can quickly and accurately identify the pollutants in the interference image, which not only helps to improve the quality evaluation of the interference image, but also provides a reliable basis for subsequent data analysis and decision-making.

Description

Technical Field

[0001] This invention relates to image recognition methods, specifically to a method for identifying contaminants in interferometric images based on parasitic image interference elimination. Background Technology

[0002] With the continuous development of large-aperture static interferometric spectral imaging technology, interferometric imaging plays an increasingly important role in scientific research and industrial applications. However, due to factors such as environmental noise and optical system errors, contaminants may appear in some areas of the interferometric image, and the contaminant characterization may be unclear. This phenomenon often leads to a significant decrease in image quality, especially in high-spectral-resolution applications. Furthermore, as... Figure 1 and Figure 2 As shown, parasitic images in interference images manifest as unnecessary artifacts or noise in the image, which can be easily confused with the characteristics of contaminants, reducing the accuracy of identification.

[0003] Therefore, eliminating interference caused by parasitic images and thus quickly and accurately identifying contaminants not only helps improve the quality assessment of interferometric images but also provides a reliable basis for subsequent data analysis and decision-making. However, existing contaminant identification methods are unable to accurately identify them, hindering the assessment of interferometric image quality. Summary of the Invention

[0004] To address the technical problem of inaccurate pollutant identification due to unclear pollutant characterization and susceptibility to parasitic image interference during the process of pollutant identification from interferometric images, this invention provides a pollutant identification method from interferometric images based on parasitic image interference elimination.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for identifying contaminants in interferometric images based on parasitic image interference cancellation, characterized by the following steps:

[0007] Step 1: Perform polynomial fitting on the interference image along the interference dimension;

[0008] For interferometric image I (H,W) Polynomial fitting along the interference dimension H yields the fitted interference image I′. (H,W) In this context, the interference dimension H represents the number of rows; the width dimension W represents the number of columns.

[0009] Step 2: Calculate the interferometric image I′ (H,W) Interference dimension correction coefficient k H ;

[0010] Step 3: Calculate the interferometric image I′ (H,W) Width dimension correction coefficient k W ;

[0011] Step 4: Correct the interference dimension using the correction coefficient k H Width dimension correction coefficient k W For interferometric image I (H,W) Relative radiometric correction is performed to obtain an interferometric image I″ that eliminates parasitic image interference;

[0012] Step 5: Use Haar wavelet to perform wavelet transform on the interference image I″ after eliminating parasitic image interference, decompose the interference image I″ into different frequency components, and obtain the vertical edge information image cV;

[0013] Step 6: Calculate the amplitude of each pixel in the vertical edge information image cV to obtain the feature intensity of each pixel in the vertical edge information image cV; sort the feature intensity of each column of pixels, and select the t-th quantile after sorting as the feature intensity benchmark, 60%≤t≤80%;

[0014] Step 7: Process the feature intensity of each pixel in the vertical edge information image cV column by column, and identify the pixels with feature intensity greater than the preset multiple of the feature intensity benchmark as the initial pollutants;

[0015] Step 8: Restore the coordinates of the initial contaminants to the interferometric image I. (H,W) From the coordinates, we obtain the coordinates of four pixels. The pixel with the smallest value is selected and identified as the interference image I. (H,W) The final contaminants; complete the contaminant identification in the interferometric image.

[0016] Furthermore, step 1 specifically includes:

[0017] For interferometric image I (H,W) Polynomial fitting along the interference dimension H yields the fitted interference image I′. (H,W) The interference dimension H represents the number of rows; the width dimension W represents the number of columns; the fitting formula is as follows:

[0018] I' j =f n (I j );

[0019] Among them, I j Represents the interferometric image I (H,W) The j-th interference curve, j = 1, 2... or W; f n () denotes an nth-order polynomial function, n≥1; I′ j The interferometric image I′ after polynomial fitting (H,W) The j-th column interference curve.

[0020] Furthermore, step 2 specifically includes:

[0021] 2.1. Regarding the interferometric image I′ (H,W) The mean vector m = (m1, m2, ..., m) is obtained by averaging along the width dimension W. i ,…,m H ) T ;in, p (i,j) Represents the interferometric image I′ (H,W) The pixel value in the i-th row and j-th column;

[0022] 2.2. Calculate the average of the mean vector m to obtain the global mean m. all :

[0023]

[0024] 2.3 Calculate the interferometric correction coefficient k using the relative radiation correction method. H :

[0025]

[0026] Furthermore, step 3 specifically includes:

[0027] 3.1. Regarding the interferometric image I′ (H,W) Averaging along the interference dimension H yields the mean vector n = (n1, n2, ..., n j ,…,n W ) T ;in,

[0028] 3.2 Calculate the swath dimension correction coefficient k using the relative radiometric correction method. W :

[0029]

[0030] Furthermore, step 4 specifically involves:

[0031] For interferometric image I (H,W) After performing relative radiometric correction, the interferometric image I″ is obtained, which eliminates parasitic image interference.

[0032] I″=I·k H ·k W .

[0033] Furthermore, step 6 specifically includes:

[0034] Calculate the amplitude cV′ of each pixel in the vertical edge information image cV. (x,y) =|cV (x,y)|, to obtain the feature intensity of each pixel in the vertical edge information image cV; where (x, y) are the coordinates of the pixel; the feature intensities of each column of pixels are sorted in ascending order, and the t-th quantile of the sorted values ​​is selected as the feature intensity benchmark cV′. y 60% ≤ t ≤ 80%.

[0035] Furthermore, step 7 specifically includes:

[0036] Perform column-by-column processing on each pixel in the vertical edge information image cV, and select pixels with feature intensity greater than Q·cV′. y The point (x) pol ,y pol ) is identified as a pollutant; where Q is a preset multiple and Q≥1.

[0037] Furthermore, step 8 specifically includes:

[0038] The initial pollutant coordinates (x, y) identified in the vertical edge information image cV are... pol ,y pol Restore to the original interferometric image I (H,W) coordinates (x) ori ,y ori )middle:

[0039]

[0040] Interference image I′ (H,W) coordinates (x) ori ,y ori It contains 4 pixels, namely and Select the pixel with the smallest pixel value and identify it as the interference image I. (H,W) The final pollutants are identified, and pollutant identification is completed in the interference image.

[0041] Furthermore, in step 1, n = 4.

[0042] Furthermore, in step 7, Q = 4;

[0043] In step 8, t = 70%.

[0044] The beneficial effects of this invention are:

[0045] 1. The present invention provides a method for identifying pollutants in interferometric images based on the elimination of parasitic image interference. By eliminating the interference of parasitic images, it avoids misjudgment caused by confusion between parasitic images and pollutant features, and significantly improves the accuracy of pollutant identification.

[0046] 2. The present invention provides a method for identifying contaminants in interferometric images based on parasitic image interference elimination, which is applicable to various types of large-aperture static interferometric spectral imaging systems. It can handle the contaminant identification problem in different scenarios, providing more reliable technical support for hyperspectral imaging in scientific research and industrial applications, and has broad application value. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the parasitic image region in the interference image. The area enclosed in (a) is the parasitic image region, and the area enclosed in (b) is the area in the interference image where the parasitic image and the pollutant features are easily confused.

[0048] Figure 2 This is a schematic diagram of some contaminants in the interference image;

[0049] Figure 3 This is a flowchart illustrating an embodiment of the present invention of an interference image contaminant identification method based on parasitic image interference elimination;

[0050] Figure 4 This is a schematic diagram of interference image elimination of parasitic image interference in an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of vertical edge information image recognition of pollutants in an embodiment of the present invention;

[0052] Figure 6 This is a schematic diagram of interferometric image recognition of pollutants in an embodiment of the present invention. Detailed Implementation

[0053] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] This invention provides a method for identifying contaminants in interferometric images based on parasitic image interference elimination. This method accurately identifies contaminants while eliminating parasitic image interference, thus improving the quality assessment of interferometric images. For example... Figure 3 As shown, the specific steps include:

[0055] Step 1: Perform polynomial fitting on the interference image along the interference dimension;

[0056] For interferometric image I (H,W) Polynomial fitting along the interference dimension H yields the fitted interference image I′. (H,W) The interference dimension H represents the number of rows; the width dimension W represents the number of columns; the fitting formula is as follows:

[0057] I' j =f n (I j );

[0058] Among them, I j Represents the interferometric image I (H,W) The j-th interference curve, j = 1, 2... or W; f n () denotes an nth-order polynomial function, n≥1; I′ j The interferometric image I′ after polynomial fitting (H,W) The j-th interference curve; in this embodiment, H = 256, W = 1024, n = 4.

[0059] Step 2: Calculate the interferometric correction coefficient k H ;

[0060] 2.1. Regarding the interferometric image I′ (H,W) The mean vector m = (m1, m2, ..., m) is obtained by averaging along the width dimension W. i ,…,m H ) T ;in, p (i,j) Represents the interferometric image I′ (H,W) The pixel value in the i-th row and j-th column;

[0061] 2.2. Calculate the average of the mean vector m to obtain the global mean m. all :

[0062]

[0063] 2.3 Calculate the interferometric correction coefficient k using the relative radiation correction method. H :

[0064]

[0065] Step 3: Calculate the width dimension correction coefficient k W ;

[0066] 3.1. Regarding the interferometric image I′ (H,W) Averaging along the interference dimension H yields the mean vector n = (n1, n2, ..., n j ,…,n W ) T ;in,

[0067] 3.2 Calculate the swath dimension correction coefficient k using the relative radiometric correction method. W :

[0068]

[0069] Step 4: Elimination of parasitic interference in the interference image;

[0070] like Figure 4 As shown, for the interference image I (H,W) After performing relative radiometric correction, the interferometric image I″ is obtained, which eliminates parasitic image interference.

[0071] I″=I·k H ·k W ;

[0072] Step 5: Wavelet transform processing;

[0073] The Haar wavelet transform is applied to the interferogram I″ after eliminating parasitic image interference, decomposing it into different frequency components to obtain the basic information image cA, the horizontal edge information image cH, the vertical edge information image cV, and the diagonal edge information image cD. Furthermore, the rows and columns of the basic information image cA, the horizontal edge information image cH, the vertical edge information image cV, and the diagonal edge information image cD are all elements of the interferogram I″.

[0074] Step 6: Calculate the feature intensity;

[0075] Calculate the amplitude cV′ of each pixel in the vertical edge information image cV. (x,y) =|cV (x,y) |, to obtain the feature intensity of each pixel in the vertical edge information image cV; where (x, y) are the coordinates of the pixel; and sort the feature intensity of each column of pixels in ascending order, selecting the t-th quantile of the sorted image as the feature intensity benchmark cV′. y 60% ≤ t ≤ 80%; in this embodiment, t = 70%;

[0076] Step 7: Pollutant identification;

[0077] Perform column-by-column processing on each pixel in the vertical edge information image cV, and select pixels with feature intensity greater than Q·cV′. y The point (x) pol ,y pol ) identified as initial contaminants, such as Figure 5 As shown; where Q is a preset multiple and Q≥1; in this embodiment, Q=4 is preferred;

[0078] Step 8: Restore coordinates;

[0079] The initial pollutant coordinates (x, y) identified in the vertical edge information image cV are... pol ,y pol Restore to the original interferometric image I (H,W) coordinates (x) ori,y ori )middle:

[0080]

[0081] Interference image I′ (H,W) coordinates (x) ori ,y ori It contains 4 pixels, namely and

[0082] like Figure 6 As shown, the pixel with the smallest pixel value is selected and identified as the interference image I. (H,W) The final pollutants.

[0083] Complete the identification of contaminants in the interference image.

[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present invention should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for identifying contaminants in interferometric images based on parasitic image interference cancellation, characterized in that, Includes the following steps: Step 1: Perform polynomial fitting on the interference image along the interference dimension; For interferometric image I (H,W) Polynomial fitting along the interference dimension H yields the fitted interference image I′. (H,W) In this context, the interference dimension H represents the number of rows; the width dimension W represents the number of columns. Step 2: Calculate the interferometric image I′ (H,W) Interference dimension correction coefficient k H ; Step 3: Calculate the interferometric image I′ (H,W) Width dimension correction coefficient k W ; Step 4: Correct the interference dimension using the correction coefficient k H Width dimension correction coefficient k W For interferometric image I (H,W) Relative radiometric correction is performed to obtain an interferometric image I″ that eliminates parasitic image interference; Step 5: Use Haar wavelet to perform wavelet transform on the interference image I″ after eliminating parasitic image interference, decompose the interference image I″ into different frequency components, and obtain the vertical edge information image cV; Step 6: Calculate the amplitude of each pixel in the vertical edge information image cV to obtain the feature intensity of each pixel in the vertical edge information image cV; sort the feature intensity of each column of pixels, and select the t-th quantile after sorting as the feature intensity benchmark, 60%≤t≤80%; Step 7: Process the feature intensity of each pixel in the vertical edge information image cV column by column, and identify the pixels with feature intensity greater than the preset multiple of the feature intensity benchmark as the initial pollutants; Step 8: Restore the coordinates of the initial contaminants to the interferometric image I. (H,W) From the coordinates, we obtain the coordinates of four pixels. The pixel with the smallest value is selected and identified as the interference image I. (H,W) The final contaminants; complete the contaminant identification in the interferometric image.

2. The method for identifying contaminants in interferometric images based on parasitic image interference cancellation according to claim 1, characterized in that, Step 1 is as follows: For interferometric image I (H,W) Polynomial fitting along the interference dimension H yields the fitted interference image I′. (H,W) The interference dimension H represents the number of rows; the width dimension W represents the number of columns; the fitting formula is as follows: I′ j =f n (I j ); Among them, I j Represents the interferometric image I (H,W) The j-th interference curve, j = 1, 2... or W; f n () denotes an nth-order polynomial function, n≥1; I′ j The interferometric image I′ after polynomial fitting (H,W) The j-th column interference curve.

3. The method for identifying contaminants in interferometric images based on parasitic image interference cancellation according to claim 2, characterized in that, Step 2 specifically includes: 2.

1. Regarding the interferometric image I′ (H,W) The mean vector m = (m1, m2, ..., m) is obtained by averaging along the width dimension W. i ,…,m H ) T ;in, p (i,j) Represents the interferometric image I′ (H,W) The pixel value in the i-th row and j-th column; 2.

2. Calculate the average of the mean vector m to obtain the global mean m. all : 2.3 Calculate the interferometric correction coefficient k using the relative radiation correction method. H :

4. The method for identifying contaminants in interferometric images based on parasitic image interference cancellation according to claim 3, characterized in that, Step 3 specifically includes: 3.

1. Regarding the interferometric image I′ (H,W) Averaging along the interference dimension H yields the mean vector n = (n1, n2, ..., n j ,…,n W ) T ;in, 3.2 Calculate the swath dimension correction coefficient k using the relative radiometric correction method. W :

5. The method for identifying contaminants in interferometric images based on parasitic image interference cancellation according to claim 4, characterized in that, Step 4 is as follows: For interferometric image I (H,W) After performing relative radiometric correction, the interferometric image I″ is obtained, which eliminates parasitic image interference. I″=I·k H ·k W 。 6. The method for identifying contaminants in interferometric images based on parasitic image interference cancellation according to claim 5, characterized in that, Step 6 specifically involves: Calculate the amplitude cV′ of each pixel in the vertical edge information image cV. (x,y) =|cV (x,y) |, to obtain the feature intensity of each pixel in the vertical edge information image cV; Where (x, y) are the coordinates of the pixel; the feature intensities of each column of pixels are sorted in ascending order, and the t-th quantile of the sorted values ​​is selected as the feature intensity benchmark cV′. y 60% ≤ t ≤ 80%.

7. The method for identifying contaminants in interferometric images based on parasitic image interference cancellation according to claim 6, characterized in that, Step 7 specifically includes: Perform column-by-column processing on each pixel in the vertical edge information image cV, and select pixels with feature intensity greater than Q·cV′. y The point (x) pol ,y pol ) is identified as a pollutant; where Q is a preset multiple and Q≥1.

8. The method for identifying contaminants in interferometric images based on parasitic image interference cancellation according to claim 7, characterized in that, Step 8 specifically includes: The initial pollutant coordinates (x, y) identified in the vertical edge information image cV are... pol ,y pol Restore to the original interferometric image I (H,W) coordinates (x) ori ,y ori )middle: Interference image I′ (H,W) coordinates (x) ori ,y ori It contains 4 pixels, namely and Select the pixel with the smallest pixel value and identify it as the interference image I. (H,W) The final pollutants are identified, and pollutant identification is completed in the interference image.

9. The method for identifying contaminants in interferometric images based on parasitic image interference cancellation according to any one of claims 2-8, characterized in that: In step 1, n = 4.

10. The method for identifying contaminants in interferometric images based on parasitic image interference cancellation according to claim 8, characterized in that: In step 7, Q = 4; In step 8, t = 70%.

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