CN113706396B - Remote sensing image noise reduction processing method based on sliding window function - Google Patents
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Abstract
The specification provides a telemetry image noise reduction processing method based on a sliding window function, which comprises the following steps: carrying out principal component analysis on the n-channel image to obtain n principal component components; converting the n principal component components into n first matrices; filtering the first matrix corresponding to the first m-order principal component by adopting a first sliding window function, and filtering the other first matrices by adopting a second sliding window function to obtain n second matrices; performing inverse conversion on the n second matrixes to obtain n filtering principal component components, and obtaining an n-channel noise reduction image according to the n filtering principal component components; wherein the window size of the first sliding window function is smaller than the window size of the second sliding window function. When the first matrix corresponding to the first m-order principal component is filtered, more image detail information can be reserved; when the first matrix corresponding to other principal component components is filtered, more high-frequency noise components can be removed, and then the n-channel noise reduction image is enabled.
Description
Technical Field
The application relates to the technical field of image processing, in particular to a telemetry image noise reduction processing method based on a sliding window function.
Background
In the telemetry image acquisition process, system noise is caused by the influence of environmental characteristics such as illumination characteristics, atmospheric turbulence characteristics and the like in the acquisition environment and the characteristic change of equipment such as an optical lens, an image sensor and the like caused by the environmental characteristics, so that the definition of telemetry images is reduced.
Because of the nature of the field of practical application, telemetry images include a large number of point, line and tip image details, and noise affects the efficient identification of such details to be removed. The existing methods for removing noise at the rear end comprise mean value filtering, median value filtering methods and the like, wherein the filtering methods are all spatial domain appearance processing methods, and the average value or the middle value of pixel points in a window is obtained by adopting window sliding in an image to replace the pixel value of a middle point.
But such filtering methods have high frequency characteristics that are easily adaptable, for example: when the filtering window is selected to be too small, noise filtering is not obvious; when the filter window is selected too large, the effective high frequency features in the image are blurred.
Disclosure of Invention
The specification provides a telemetry image noise reduction processing method and device based on a sliding window function, so as to solve the problem of need choice caused by selecting a filtering window in the background art.
The specification provides a telemetry image noise reduction processing method based on a sliding window function, which comprises the following steps:
carrying out principal component analysis on the n-channel image to obtain n principal component components;
respectively converting the n principal component components into n first matrixes with the same size as the n-channel image;
Filtering m first matrixes corresponding to the first m-order principal component components by adopting a first sliding window function, and filtering other first matrixes by adopting a second sliding window function to obtain n second matrixes;
Performing inverse conversion on the n second matrixes to obtain n filtering principal component components, and obtaining an n-channel noise reduction image according to the n filtering principal component components;
wherein the window size of the first sliding window function is smaller than the window size of the second sliding window function.
Optionally, performing principal component analysis on the n-channel image to obtain n principal component components, including:
Transforming the jth color channel of the n-channel image into a one-dimensional vector x i, and forming a third matrix using the one-dimensional vector x i
Calculating a covariance matrix C x of the third matrix and calculating a eigenvector matrix V corresponding to the covariance matrix C x;
Calculating a fourth matrix PC by adopting the eigenvector matrix V and the n-channel image, wherein the fourth matrix PC=V T x, and n principal component components are obtained according to the fourth matrix PC;
Obtaining an n-channel noise reduction image from the n filtered principal component components, comprising:
Adopting n filtered principal component components to form n fifth matrixes PC';
Obtaining a sixth matrix by using the fifth matrix PC' and the eigenvector matrix V
And inversely transforming y j in the sixth matrix into a j-th color channel of the n-channel noise reduction image to obtain the n-channel noise reduction image.
Alternatively, n=3;
median filtering and/or mean filtering are carried out on m two-dimensional matrixes corresponding to the first m-order principal component components by adopting a first sliding window, and the method comprises the following steps:
And carrying out median filtering and/or mean filtering on the two-dimensional matrix corresponding to the first-order principal component by adopting a first sliding window.
Optionally, median filtering and/or mean filtering are performed on m two-dimensional matrices corresponding to the first m-order principal component components by using a first sliding window, and median filtering and/or mean filtering are performed on other two-dimensional matrices by using a second sliding window, including:
when median filtering and/or mean filtering are carried out on the edges of m two-dimensional matrixes corresponding to the first m-order principal component components, zero padding is carried out according to the size of a first sliding window; and
And when median filtering and/or mean filtering are carried out on other two-dimensional matrixes, zero padding is carried out according to the second sliding window size.
Optionally, the first sliding window function is a median filter function or a mean filter function; the second sliding window function is a median filter function or a mean filter function.
The present specification provides a noise reduction processing apparatus for a telemetry image, including:
the principal component analysis unit is used for carrying out principal component analysis on the n-channel image to obtain n principal component components;
A conversion unit configured to convert n principal component components into n first matrices of the same size as the n-channel image, respectively;
The filtering unit is used for filtering m first matrixes corresponding to the first m-order principal component components by adopting a first sliding window function, and filtering other first matrixes by adopting a second sliding window function to obtain n second matrixes;
the inverse conversion unit is used for carrying out inverse conversion on the n second matrixes to obtain n filtering principal component components, and obtaining n channel noise reduction images according to the n filtering principal component components;
wherein the window size of the first sliding window function is smaller than the window size of the second sliding window function.
Optionally, the principal component analysis unit includes:
a first subunit for transforming the jth color channel of the n-channel image into a one-dimensional vector x i and forming a third matrix using the one-dimensional vector x i
A second subunit, configured to calculate a covariance matrix C x of the third matrix, and calculate a eigenvector matrix V corresponding to the covariance matrix C x;
a third subunit, configured to calculate a fourth matrix PC using the feature vector matrix V and the n-channel image, where the fourth matrix pc=v T x, and obtain n principal component components according to the fourth matrix PC;
The reverse conversion unit includes:
A fourth subunit, configured to use n filtered principal component components to form n fifth matrices PC';
a fifth subunit, configured to obtain a sixth matrix by using the fifth matrix PC' and the eigenvector matrix V
And a sixth subunit, configured to inverse-transform y j in the sixth matrix to a jth color channel of the n-channel noise-reduced image, so as to obtain the n-channel noise-reduced image.
Optionally, the filtering unit performs zero padding according to a first sliding window size when performing median filtering and/or mean filtering on edges of m two-dimensional matrices corresponding to the first m-order principal component components, and performs zero padding according to the second sliding window size when performing median filtering and/or mean filtering on other two-dimensional matrices.
Because n principal component components are obtained by principal component analysis, the effective information of the image corresponding to the principal component components of the previous stage is more, and the noise information is less; the effective information of the image corresponding to the main component of the later stage is less, and the noise information is more; wherein noise is a random high frequency component. Correspondingly, when the first matrix corresponding to the first m-order principal component components is filtered, a first sliding window function with a smaller window is adopted for processing, so that more image detail information can be reserved; and when the first matrix corresponding to other principal component components is filtered, a second sliding window function with a larger window is adopted for processing, so that more high-frequency noise components can be removed. In summary, the solution provided in this specification can be according to
In the invention, the technical schemes can be mutually combined to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
FIG. 1 is a flow chart of a method for noise reduction processing of a telemetry image based on a sliding window function provided by an embodiment;
FIG. 2 is a schematic diagram of a noise reduction processing apparatus for telemetry images provided by an embodiment;
wherein: 11-principal component analysis unit, 12-conversion unit, 13-filter unit, 14-inverse conversion unit.
Detailed Description
The following detailed description of preferred embodiments of the application is made in connection with the accompanying drawings, which form a part hereof, and together with the description of the embodiments of the application, are used to explain the principles of the application and are not intended to limit the scope of the application.
The present specification provides a method for noise reduction processing of a telemetry image based on a sliding window function, which decomposes an original telemetry image into a plurality of component components, performs respective different filtering processing for each component, and synthesizes a noise reduction image by using the plurality of component components.
Fig. 1 is a flowchart of a telemetry image noise reduction processing method based on a sliding window function according to an embodiment. As shown in fig. 1, the noise reduction processing method provided in the present embodiment includes steps S101 to S104.
S101: and carrying out principal component analysis on the n-channel image to obtain n principal component components.
In this embodiment, n is at least 3. In a specific application, the n-channel image can be an RGB image meeting the vision of human eyes, and can also be said to be an image with multi-frequency spectrum identification such as infrared spectrum band, ultraviolet spectrum band and the like. The step of performing principal component analysis on the n-channel image includes steps S1011 to S1013.
The n-channel image may be an image of various storage formats, and the present specification is not particularly limited. In practical applications, the n-channel image may be in various storage formats such as BMP, JPEG, PNG, CUR, JPEG 2000, PPM, GIF, PBM, RAS, HDF4, PCX, TIFF, ICO, PGM, XWD, etc.
In practical applications, the data type of each channel in the n-channel image may be unit8, unit16 or logical, etc. (i.e. the bit depth of each channel is the above type). In order to avoid data overflow caused in the subsequent operation process, each channel can be converted into a double type between 0 and 1.
S1011: each channel in the n-channel image is transformed into a one-dimensional vector, and all the one-dimensional vectors form a third matrix.
Assuming that the size of the n-channel image is p×q, it can be known that the length of the one-dimensional vector x i obtained by the jth color channel transform is p×q. After each color channel is transformed into a one-dimensional vector, a third matrix is formed as
S1012: and calculating a covariance matrix corresponding to the third matrix and a eigenvector matrix corresponding to the covariance matrix.
The third matrix corresponds to covariance matrix C x, where the elements in covariance matrix are C x(i,j)＝E[(xi-E(xi))(xj-E(xj).
After obtaining the covariance matrix C x, performing feature decomposition on the covariance matrix to obtain a eigenvalue diagonal matrix D and an eigenvector matrix V, where C x ·v=v·d. The eigenvalues corresponding to the principal component components of each order in the diagonal array D are arranged in descending order, and the column vectors in V are eigenvectors of the principal component components of each order.
S1013: and calculating by adopting the eigenvector matrix C and the n-channel image to obtain a fourth matrix, and obtaining n principal component components according to the fourth matrix.
The fourth matrix is denoted by PC, pc=vt x. Correspondingly, each row vector in the fourth matrix PC has a principal component.
The principal component components obtained by the decomposition processing in the method of steps S1011 to S1013 are decomposed into n principal components in a manner different from n channels.
S102: the n principal component components are converted into n first matrices of the same size as the n-channel image, respectively.
As is clear from step S101, the number of elements in each of the n principal component parts is p×q. A process of converting n principal component components into n first matrices, i.e., a process of reconverting a one-dimensional vector into a p×q matrix. In the process of conversion into the first matrix, it is necessary to ensure that the conversion process performs conversion in accordance with the conversion process reverse to step S1011 to ensure correspondence with the n-channel image pixels.
S103: and filtering m first matrixes corresponding to the first m-order principal component components by adopting a first sliding window function, and filtering other first matrixes by adopting a second sliding window function to obtain n second matrixes.
In this embodiment, the window size of the first sliding window function is smaller than the window size of the second sliding window function.
In this embodiment, the first sliding window function and the second sliding window function may be median filtering functions or mean filtering functions, which are not particularly limited in this specification. In practical application, the type of the corresponding filtering function can be adaptively selected according to the characteristics of each matrix.
In a specific application, when performing filtering processing on the edge area of each first matrix, zero padding processing is required according to the size of the sliding window.
In a specific application of this embodiment, n=3, m=1 and the first sliding window function is a5×5 window function, so when filtering two rows and two columns on the edge of the first-order first matrix, two rows or two columns of zero padding needs to be added outside the rows or columns.
S104: and carrying out inverse conversion on the n second matrixes to obtain n filtering principal component components.
Step S104 is the reverse operation of the aforementioned step S102. The corresponding method of operation is reversed from step S102.
S105: and forming an n-channel noise reduction image according to the n filtering principal component components.
Step S105 may include steps S1051-S1053.
S1051: n filter principal component components are employed to form n fifth matrices PC'.
It should be noted that when the fifth matrix PC 'is composed of n filter principal component components, the positions of the respective filter principal component components in the fifth matrix PC' are the same as the positions of the principal component components in the corresponding step S101.
S1052: and obtaining a sixth matrix by adopting the fifth matrix and the eigenvector matrix V.
In the present embodiment, the sixth matrix isRepresentation, sixth matrix/>
S1053: and inversely transforming y j in the sixth matrix into a j-th color channel of the n-channel noise reduction image to obtain the n-channel noise reduction image.
Step S1053 is the reverse operation of the aforementioned step S1011. Where y j is inverse transformed to the j-th color channel of the n-channel noise-reduced image, a p×q matrix is also required to be formed. After n p multiplied by q matrixes are obtained, combining the n matrixes according to the corresponding color channels to obtain an n-channel noise reduction image.
Referring to the foregoing steps S101 and S102, since n principal component components are obtained by principal component analysis, the effective information of the image corresponding to the principal component of the previous stage is more, and the noise information is less; the effective information of the image corresponding to the main component of the later stage is less, and the noise information is more; wherein noise is a random high frequency component.
Correspondingly, when the first matrix corresponding to the first m-order principal component components is filtered in the step S103, a first sliding window function with a smaller window is adopted for processing, so that more image detail information can be reserved; and when the first matrix corresponding to other principal component components is filtered, a second sliding window function with a larger window is adopted for processing, so that more high-frequency noise components can be removed. Therefore, by adopting the method of the steps S101-S105, the noise in the image can be removed better, and the effective high-frequency detail information of the image can be kept. Thus improving the quality of the telemetry image noise reduction process.
In addition to providing the aforementioned noise reduction processing method for the telemetry image based on the sliding window function, the present description provides a noise reduction processing device for the telemetry image. The processing apparatus employs the same inventive concept as the aforementioned processing method. Hereinafter, only the structure of the treatment device in the embodiment of the present disclosure will be described, and the technical problems and effects to be solved accordingly may be described in the foregoing.
Fig. 2 is a schematic diagram of a noise reduction processing device for a telemetry image according to an embodiment. As shown in fig. 2, the processing apparatus provided in the present embodiment includes a principal component analysis unit 11, a conversion unit 12, a filtering unit 13, and a reverse conversion unit 14.
The principal component analysis unit 11 is configured to perform principal component analysis on the n-channel image to obtain n principal component components.
The conversion unit 12 is configured to convert the n principal component components into n first matrices of the same size as the n-channel image, respectively.
The filtering unit 13 is configured to filter m first matrices corresponding to the first m-order principal component components by using a first sliding window function, and filter other first matrices by using a second sliding window function, so as to obtain n second matrices.
The inverse conversion unit 14 is configured to perform inverse conversion on the n second matrices to obtain n filtered principal component components, and form an n-channel noise reduction image according to the n filtered principal component components;
wherein the window size of the first sliding window function is smaller than the window size of the second sliding window function.
In one application, the principal component analysis unit 11 includes a first subunit, a second subunit, and a third subunit, and the inverse conversion unit 14 includes a fourth subunit, a fifth subunit, and a sixth subunit.
The first subunit is configured to transform the jth color channel of the n-channel image into a one-dimensional vector x i and form a third matrix using the one-dimensional vector x i The second subunit is configured to calculate a covariance matrix C x of the third matrix, and calculate a eigenvector matrix V corresponding to the covariance matrix C x; the third subunit is configured to calculate a fourth matrix PC using the feature vector matrix V and the n-channel image, where the fourth matrix pc=vt x, and obtain n principal component components according to the fourth matrix PC.
The fourth subunit is configured to use n filtered principal component components to form n fifth matrices PC'; the fifth subunit is configured to obtain a sixth matrix by using the fifth matrix PC' and the eigenvector matrix VThe sixth subunit is configured to inverse-transform y j in the sixth matrix to a j-th color channel of the n-channel noise-reduced image, so as to obtain the n-channel noise-reduced image.
In one application, the filtering unit 13 performs zero padding processing according to the first sliding window size when median filtering and/or mean filtering is performed on edges of m two-dimensional matrices corresponding to the m-th order principal component components, and performs zero padding processing according to the second sliding window size when median filtering and/or mean filtering is performed on other two-dimensional matrices.
In addition to providing the aforementioned noise reduction processing device for telemetry images, the embodiments of the present specification provide an electronic device. The electronic device includes a memory and a processor; the memory stores program instructions, and the processor can execute the telemetry image noise reduction processing method based on the sliding window function after loading the program instructions.
In addition, the embodiments of the present specification also provide a computer-readable storage medium; the computer readable storage medium stores program instructions; the program instructions can be loaded by a computer to execute the telemetry image noise reduction processing method based on the sliding window function.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (9)
1. A method for noise reduction processing of a telemetry image based on a sliding window function, comprising:
performing principal component analysis on the n-channel image to obtain n principal component components, including:
Transforming the jth color channel of the n-channel image into a one-dimensional vector x i, and forming a third matrix using the one-dimensional vector x i
Calculating a covariance matrix C x of the third matrix and calculating a eigenvector matrix V corresponding to the covariance matrix C x;
Calculating a fourth matrix PC by adopting the eigenvector matrix V and the n-channel image, wherein the fourth matrix PC=V T x, and n principal component components are obtained according to the fourth matrix PC;
Obtaining an n-channel noise reduction image from the n filtered principal component components, comprising:
Adopting n filtered principal component components to form n fifth matrixes PC';
Obtaining a sixth matrix by using the fifth matrix PC' and the eigenvector matrix V
Inversely transforming y j in the sixth matrix to a j-th color channel of the n-channel noise reduction image to obtain the n-channel noise reduction image; respectively converting the n principal component components into n first matrixes with the same size as the n-channel image;
noise reduction and filtering are carried out on m first matrixes corresponding to the first m-order principal component components by adopting a first sliding window function, noise reduction and filtering are carried out on other first matrixes by adopting a second sliding window function, and n second matrixes are obtained;
Performing inverse conversion on the n second matrixes to obtain n filtering principal component components, and obtaining an n-channel noise reduction image according to the n filtering principal component components;
Wherein m < n, the window size of the first sliding window function is smaller than the window size of the second sliding window function.
2. The method according to claim 1, characterized in that:
n＝3；
median filtering and/or mean filtering are carried out on m two-dimensional matrixes corresponding to the first m-order principal component components by adopting a first sliding window, and the method comprises the following steps:
And carrying out median filtering and/or mean filtering on the two-dimensional matrix corresponding to the first-order principal component by adopting a first sliding window.
3. The method according to claim 1, characterized in that:
Median filtering and/or mean filtering are carried out on m two-dimensional matrixes corresponding to the first m-order principal component components by adopting a first sliding window, and median filtering and/or mean filtering are carried out on other two-dimensional matrixes by adopting a second sliding window, and the method comprises the following steps:
when median filtering and/or mean filtering are carried out on the edges of m two-dimensional matrixes corresponding to the first m-order principal component components, zero padding is carried out according to the size of a first sliding window; and
And when median filtering and/or mean filtering are carried out on other two-dimensional matrixes, zero padding is carried out according to the second sliding window size.
4. The method according to claim 1, characterized in that:
The first sliding window function is a median filter function or a mean filter function; the second sliding window function is a median filter function or a mean filter function.
5. A noise reduction processing apparatus for a telemetry image, comprising:
A principal component analysis unit for performing principal component analysis on the n-channel image to obtain n principal component components, including:
Transforming the jth color channel of the n-channel image into a one-dimensional vector x i, and forming a third matrix using the one-dimensional vector x i
Calculating a covariance matrix C x of the third matrix and calculating a eigenvector matrix V corresponding to the covariance matrix C x;
Calculating a fourth matrix PC by adopting the eigenvector matrix V and the n-channel image, wherein the fourth matrix PC=V T x, and n principal component components are obtained according to the fourth matrix PC;
Obtaining an n-channel noise reduction image from the n filtered principal component components, comprising:
Adopting n filtered principal component components to form n fifth matrixes PC';
Obtaining a sixth matrix by using the fifth matrix PC' and the eigenvector matrix V
Inversely transforming y j in the sixth matrix to a j-th color channel of the n-channel noise reduction image to obtain the n-channel noise reduction image;
A conversion unit configured to convert n principal component components into n first matrices of the same size as the n-channel image, respectively;
The filtering unit is used for carrying out noise reduction and filtering on m first matrixes corresponding to the first m-order principal component components by adopting a first sliding window function, and carrying out noise reduction and filtering on other first matrixes by adopting a second sliding window function, so as to obtain n second matrixes;
the inverse conversion unit is used for carrying out inverse conversion on the n second matrixes to obtain n filtering principal component components, and obtaining n channel noise reduction images according to the n filtering principal component components;
wherein the window size of the first sliding window function is smaller than the window size of the second sliding window function.
6. The apparatus according to claim 5, wherein the principal component analysis unit includes:
a first subunit for transforming the jth color channel of the n-channel image into a one-dimensional vector x i and forming a third matrix using the one-dimensional vector x i
A second subunit, configured to calculate a covariance matrix C x of the third matrix, and calculate a eigenvector matrix V corresponding to the covariance matrix C x;
a third subunit, configured to calculate a fourth matrix PC using the feature vector matrix V and the n-channel image, where the fourth matrix pc=v T x, and obtain n principal component components according to the fourth matrix PC;
The reverse conversion unit includes:
a fourth subunit, configured to use n filtered principal component components to form n fifth matrices PC';
a fifth subunit, configured to obtain a sixth matrix by using the fifth matrix PC' and the eigenvector matrix V
And a sixth subunit, configured to inverse-transform y j in the sixth matrix to a jth color channel of the n-channel noise-reduced image, so as to obtain the n-channel noise-reduced image.
7. The apparatus according to claim 5 or 6, wherein,
And the filtering unit performs zero padding according to the first sliding window size when performing median filtering and/or mean filtering on the edges of m two-dimensional matrixes corresponding to the first m-order principal component components, and performs zero padding according to the second sliding window size when performing median filtering and/or mean filtering on other two-dimensional matrixes.
8. An electronic device includes a memory and a processor; program instructions are stored in the memory; the processor being configured to load the program instructions to perform the method of any of claims 1-4.
9. A computer readable storage medium storing program instructions; which program instructions, when loaded by a processor, are adapted to carry out the method according to any of claims 1-4.
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