CN115661006A - Seabed landform image denoising method - Google Patents

Seabed landform image denoising method Download PDF

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CN115661006A
CN115661006A CN202211700984.5A CN202211700984A CN115661006A CN 115661006 A CN115661006 A CN 115661006A CN 202211700984 A CN202211700984 A CN 202211700984A CN 115661006 A CN115661006 A CN 115661006A
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image
wavelet
morphological
submarine
frequency
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CN115661006B (en
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陈路
朱小龙
邓佳俊
杨睿
陈卓
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Hunan Guotian Electronic Technology Co ltd
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Abstract

The invention relates to the technical field of image denoising, and discloses a method for denoising a submarine landform image, which comprises the following steps: constructing a morphological wavelet based on a midpoint filter in a nonlinear filter; based on the constructed morphological wavelet, performing morphological wavelet decomposition on the submarine landform image to be processed to obtain a decomposed wavelet signal, and performing wavelet reconstruction on the wavelet signal after removing a high-frequency part in the wavelet signal to obtain a submarine landform image after wavelet reconstruction; carrying out gray level adjustment on the seabed landform image after wavelet reconstruction by using a gray level correction method; and performing contrast enhancement on the submarine landform image after gray correction based on an improved Gamma adjustment algorithm. The method of the invention removes the white Gaussian noise in the submarine landform image by using the morphological wavelet, and the image processed by the morphological wavelet is easy to cause color distortion, and simultaneously realizes the contrast enhancement of the image by using a gray correction method and a Gamma adjustment algorithm.

Description

Seabed landform image denoising method
Technical Field
The invention relates to the technical field of image denoising, in particular to a seabed landform image denoising method.
Background
Sonar imaging is an important means for mapping submarine landforms, but the effect of the sonar imaging is seriously affected by propagation media, noise interference and irregular shapes of the submarine landforms, and although the existing digital image processing can effectively denoise digital images, the sonar images have the following characteristics, so that the existing optical image processing technology cannot be directly applied to the sonar images. 1. The underwater environment is complex and has more noise, so that the gray level of a target object is less, but the gray level of background noise is rich; 2. the imaging resolution is low due to the defect of an accepted array in the imaging sonar equipment; 3. the underwater propagation medium is floating, and the sound waves received by the matrix can be weakened and incomplete, so that imaging defects are caused; 4. the sonar image generally has shadow and occupies most area, and the imaging effect is influenced. Aiming at the characteristics of sonar images, the invention provides a seabed landform image denoising method, which improves the imaging effect of the imaged seabed landform.
Disclosure of Invention
The invention provides a method for denoising a submarine relief image, and aims to (1) remove high-frequency noise of the submarine relief image; and (2) realizing contrast enhancement of the submarine landform image.
The invention provides a method for denoising a submarine landform image, which comprises the following steps:
s1: constructing morphological wavelets based on a midpoint filter in a nonlinear filter, and improving the removal effect of Gaussian white noise;
s2: based on the constructed morphological wavelet, performing morphological wavelet decomposition on the submarine geomorphic image to be processed to obtain a decomposed wavelet signal, and performing wavelet reconstruction on the wavelet signal after removing a high-frequency part in the wavelet signal to obtain the submarine geomorphic image after wavelet reconstruction;
s3: carrying out gray level adjustment on the seabed landform image after wavelet reconstruction by using a gray level correction method;
s4: and performing contrast enhancement on the submarine landform image after the gray level correction based on an improved Gamma adjustment algorithm to obtain the submarine landform image after the final denoising treatment.
As a further improvement of the method of the invention:
in the step S1, morphological wavelets are constructed based on a midpoint filter in a nonlinear filter, including:
the morphological wavelet comprises a high-frequency morphological wavelet and a low-frequency morphological wavelet, the high-frequency morphological wavelet comprises a horizontal morphological wavelet, a vertical morphological wavelet and a diagonal morphological wavelet, and the morphological wavelet input value based on the midpoint filter is an image pixel
Figure 788174DEST_PATH_IMAGE001
Wherein
Figure 236473DEST_PATH_IMAGE001
The image pixel of the mth row and the nth column of the image is represented, and the filtering result of the high-frequency morphological wavelet is as follows:
Figure 547368DEST_PATH_IMAGE002
Figure 12020DEST_PATH_IMAGE003
Figure 91972DEST_PATH_IMAGE004
wherein:
Figure 445593DEST_PATH_IMAGE005
representing an image pixel
Figure 243784DEST_PATH_IMAGE001
The horizontal morphological wavelet decomposition result of (1);
Figure 724576DEST_PATH_IMAGE006
representing image pixels
Figure 924613DEST_PATH_IMAGE001
The vertical morphology wavelet decomposition result of (3);
Figure 449135DEST_PATH_IMAGE007
representing image pixels
Figure 469044DEST_PATH_IMAGE001
The result of the diagonal wavelet decomposition;
the filtering result of the low-frequency morphological wavelet is as follows:
Figure 252061DEST_PATH_IMAGE008
Figure 41025DEST_PATH_IMAGE009
Figure 2028DEST_PATH_IMAGE010
wherein:
Figure 774812DEST_PATH_IMAGE011
representing an image pixel
Figure 331826DEST_PATH_IMAGE001
The low-frequency morphological wavelet decomposition result of (3).
In the step S2, morphological wavelet decomposition is performed on the submarine landform image to be processed based on morphological wavelets to obtain decomposed wavelet signals, including:
according to the constructed morphological wavelet, performing morphological wavelet decomposition on the seabed landform image to be processed, wherein the image pixels of any mth row and nth column in the seabed landform image to be processed are expressed as
Figure 506456DEST_PATH_IMAGE012
Using low-frequency morphological wavelets and high-frequency morphological wavelet pairs, respectively
Figure 107201DEST_PATH_IMAGE012
Performing morphological wavelet decomposition to obtain
Figure 367281DEST_PATH_IMAGE012
The low-frequency morphological wavelet decomposition result of (1):
Figure 977254DEST_PATH_IMAGE013
，
and high-frequency morphological wavelet decomposition results:
Figure 255658DEST_PATH_IMAGE014
；
wherein
Figure 558463DEST_PATH_IMAGE015
Representing an image pixel
Figure 774681DEST_PATH_IMAGE012
As a result of the horizontal morphological wavelet decomposition of (a),
Figure 188344DEST_PATH_IMAGE016
representing an image pixel
Figure 806408DEST_PATH_IMAGE012
As a result of the vertical-morphology wavelet decomposition of (1),
Figure 296426DEST_PATH_IMAGE017
representing an image pixel
Figure 999940DEST_PATH_IMAGE012
The result of the diagonal wavelet decomposition; the wavelet decomposition result is a wavelet signal obtained by decomposition;
computing image pixels
Figure 217294DEST_PATH_IMAGE012
Filter optimization value of (2):
Figure 955443DEST_PATH_IMAGE018
wherein:
Figure 849319DEST_PATH_IMAGE019
representing a median of the computed data sequence;
preserving high frequency morphological wavelet decomposition results
Figure 305708DEST_PATH_IMAGE020
The low-frequency morphological wavelet decomposition result
Figure 795595DEST_PATH_IMAGE021
As input value of morphological wavelet, decomposing to obtain
Figure 388251DEST_PATH_IMAGE021
High frequency morphological wavelet decomposition results of
Figure 954492DEST_PATH_IMAGE023
And low frequency morphological wavelet decomposition results
Figure 898177DEST_PATH_IMAGE024
。
In the step S2, wavelet reconstruction is performed on the wavelet signal after the high frequency part in the wavelet signal is removed, and the wavelet reconstruction includes:
setting a threshold function:
Figure 457335DEST_PATH_IMAGE025
wherein:
s represents a high-frequency morphological wavelet decomposition result, including a wavelet decomposition result of a horizontal morphological wavelet, a vertical morphological wavelet and a diagonal morphological wavelet;
Figure 904497DEST_PATH_IMAGE026
representing a value obtained by removing a high frequency part from a high frequency morphological wavelet decomposition result;
Figure 625328DEST_PATH_IMAGE027
which represents a wavelet threshold value, is a function of,
Figure 305577DEST_PATH_IMAGE028
indicates the adjustment parameter, will
Figure 402846DEST_PATH_IMAGE028
Set to 6;
wavelet decomposition result of high-frequency morphology by using threshold function
Figure 235673DEST_PATH_IMAGE029
And
Figure 861826DEST_PATH_IMAGE030
，
Figure 530836DEST_PATH_IMAGE031
is processed to be lower than the threshold value
Figure 431796DEST_PATH_IMAGE027
The high-frequency morphological wavelet decomposition result of (2) is set to 0, and the high-frequency morphological wavelet decomposition result after the high-frequency part removal is obtained:
Figure 853550DEST_PATH_IMAGE032
Figure 181763DEST_PATH_IMAGE033
reconstructing the submarine landform image according to the low-frequency morphological wavelet decomposition result and the high-frequency morphological wavelet decomposition result of the image pixel, wherein the reconstruction formula of the submarine landform image is as follows:
Figure 56178DEST_PATH_IMAGE034
Figure 10097DEST_PATH_IMAGE035
Figure 551936DEST_PATH_IMAGE036
Figure 51051DEST_PATH_IMAGE037
Figure 412762DEST_PATH_IMAGE038
wherein:
Figure 406257DEST_PATH_IMAGE039
image pixels of any mth row and nth column in the reconstructed submarine landform image are represented;
Figure 802603DEST_PATH_IMAGE040
representing an image pixel
Figure 472619DEST_PATH_IMAGE041
The filter optimization value of (1).
In the step S3, the graying processing is performed on the submarine landform image by using a grayscale correction method, and includes:
in an embodiment of the present invention, the gray scale correction method includes an image graying process and a gray scale adjustment; the grayscale processing flow of the submarine landform image comprises the following steps:
for any pixel in the reconstructed submarine landform image
Figure 321626DEST_PATH_IMAGE039
Three colors ofThe maximum value is calculated by the channel component, the maximum value is set as the gray value of the pixel point, a gray level image of the reconstructed submarine landform image is obtained, and the formula of the graying processing is as follows:
Figure 102500DEST_PATH_IMAGE042
wherein:
Figure 868200DEST_PATH_IMAGE039
representing the image pixels of the nth row of any mth line in the reconstructed submarine relief image;
Figure 709117DEST_PATH_IMAGE043
are respectively pixel points
Figure 311000DEST_PATH_IMAGE039
Values in the three color channels R, G, B;
Figure 629986DEST_PATH_IMAGE044
is a pixel point
Figure 751656DEST_PATH_IMAGE039
The gray value of (a).
In the step S3, the grayscale adjustment is performed on the submarine landform image by using a grayscale correction method, including:
calculating the gray average value of the submarine landform image in a logarithmic domain:
Figure 763475DEST_PATH_IMAGE045
wherein:
m represents the pixel line number of the submarine relief image;
n represents the pixel column number of the submarine landform image;
w represents a constant, which is set to 1;
setting a gray level adjustment coefficient:
Figure 587074DEST_PATH_IMAGE046
wherein:
Figure 975330DEST_PATH_IMAGE047
represents a constant, which is set to 0.1;
Figure 935196DEST_PATH_IMAGE048
representing the maximum gray value of the submarine relief image;
Figure 367183DEST_PATH_IMAGE049
representing image pixels in an undersea relief image
Figure 943658DEST_PATH_IMAGE039
The gray scale adjustment coefficient of (a);
utilizing gray scale adjustment coefficient to carry out adjustment on any pixel in submarine landform image
Figure 604447DEST_PATH_IMAGE039
Carrying out gray level adjustment, wherein the gray level adjustment formula is as follows:
Figure 684398DEST_PATH_IMAGE050
wherein:
Figure 54331DEST_PATH_IMAGE051
representing an arbitrary pixel
Figure 852522DEST_PATH_IMAGE039
Adjusting image pixels in RGB color channels; the obtained image with adjusted gray scale is
Figure 582581DEST_PATH_IMAGE052
。
In the step S4, the contrast enhancement is carried out on the submarine landform image based on the improved Gamma adjustment algorithm, and the method comprises the following steps:
the improved Gamma adjustment algorithm flow comprises the following steps:
1) Dividing the submarine landform image I into Q continuous and non-overlapping sub-regions;
2) Calculating a gray level histogram of each subregion;
3) Calculate the average number of pixels per sub-region:
Figure 251460DEST_PATH_IMAGE053
wherein:
num represents the total number of pixels in the sub-region;
l represents the number of gray levels of the sub-region;
set the enhancement threshold to
Figure 41561DEST_PATH_IMAGE054
Making the gray level histogram of the subregion larger than
Figure 576317DEST_PATH_IMAGE054
Truncating the pixel number, adding the truncated pixel number, and averagely distributing the pixel number to each gray level;
4) Calculating the probability of different gray level pixels in the gray level histogram of each subarea after pixel distribution:
calculating the probability of different gray level pixels in the gray level histogram of each subarea after pixel distribution:
Figure 578908DEST_PATH_IMAGE055
wherein:
i represents a gray level, and L gray levels are total;
Figure 633451DEST_PATH_IMAGE056
representing the number of pixels with the gray level i in the sub-area;
num represents the total number of pixels in the sub-region;
Figure 594454DEST_PATH_IMAGE057
a probability of occurrence of a pixel representing a gray level i in the sub-region;
the probability accumulation distribution graph PD of the sub-region gray scale is
Figure 852391DEST_PATH_IMAGE058
(ii) a Performing Gamma adjustment on the probability accumulation distribution map PD of each sub-region, and obtaining the adjusted probability accumulation distribution map
Figure 924253DEST_PATH_IMAGE059
Comprises the following steps:
Figure 98882DEST_PATH_IMAGE060
wherein:
Figure 965207DEST_PATH_IMAGE061
represents a Gamma value, which is set to 0.3;
5) And combining the sub-region images after Gamma adjustment to obtain the final denoised submarine landform image.
Compared with the prior art, the invention provides a method for denoising a submarine landform image, which has the following advantages:
firstly, the scheme provides an image white Gaussian noise removing method based on morphological wavelets, compared with the traditional wavelets, only two pixel points of an image are associated during image denoising, so that image detail analysis is not thorough, and partial image information is lostMorphological wavelet input value based on midpoint filter is image pixel
Figure 474554DEST_PATH_IMAGE062
Wherein
Figure 818948DEST_PATH_IMAGE062
The image pixel of the mth row and the nth column of the image is represented, and the filtering result of the high-frequency morphological wavelet is as follows:
Figure 848084DEST_PATH_IMAGE002
Figure 619731DEST_PATH_IMAGE003
Figure 367107DEST_PATH_IMAGE004
wherein:
Figure 531503DEST_PATH_IMAGE063
representing image pixels
Figure 415146DEST_PATH_IMAGE062
The horizontal morphological wavelet decomposition result of (1);
Figure 623273DEST_PATH_IMAGE064
representing image pixels
Figure 592366DEST_PATH_IMAGE062
The vertical morphology wavelet decomposition result of (1);
Figure 544142DEST_PATH_IMAGE065
representing image pixels
Figure 797137DEST_PATH_IMAGE062
The diagonal form wavelet decomposition result of (1); the filtering result of the low-frequency morphological wavelet is as follows:
Figure 176166DEST_PATH_IMAGE066
Figure 366976DEST_PATH_IMAGE068
Figure 122442DEST_PATH_IMAGE070
wherein:
Figure 731409DEST_PATH_IMAGE071
representing an image pixel
Figure 281339DEST_PATH_IMAGE062
The low frequency morphological wavelet decomposition result of (1). According to the constructed morphological wavelet, performing morphological wavelet decomposition on the seabed landform image to be processed, wherein the image pixels of any mth row and nth column in the seabed landform image to be processed are expressed as
Figure 490604DEST_PATH_IMAGE072
Using low-frequency morphological wavelets and high-frequency morphological wavelet pairs, respectively
Figure 518603DEST_PATH_IMAGE072
Performing morphological wavelet decomposition to obtain
Figure 231344DEST_PATH_IMAGE072
Low frequency morphological wavelet decomposition results of
Figure 201443DEST_PATH_IMAGE073
And high frequency morphological wavelet decomposition results
Figure 632424DEST_PATH_IMAGE074
Wherein
Figure 729693DEST_PATH_IMAGE075
Representing image pixels
Figure 296941DEST_PATH_IMAGE072
As a result of the horizontal morphological wavelet decomposition of (a),
Figure 204985DEST_PATH_IMAGE076
representing image pixels
Figure 857683DEST_PATH_IMAGE072
As a result of the vertical-morphology wavelet decomposition of (1),
Figure 493064DEST_PATH_IMAGE077
representing image pixels
Figure 180397DEST_PATH_IMAGE072
The wavelet decomposition result of the diagonal line state is a wavelet signal obtained by decomposition; preserving high frequency morphological wavelet decomposition results
Figure 508610DEST_PATH_IMAGE078
Figure 632293DEST_PATH_IMAGE079
The wavelet decomposition result of the low frequency morphology
Figure 336944DEST_PATH_IMAGE080
As input value of morphological wavelet, decomposing to obtain
Figure 878783DEST_PATH_IMAGE080
High frequency morphological wavelet decomposition results of
Figure 112319DEST_PATH_IMAGE081
And low frequency morphological wavelet decomposition results
Figure 755921DEST_PATH_IMAGE082
. The scheme is characterized in that the following threshold functions are set:
Figure 467525DEST_PATH_IMAGE025
wherein: s represents a high-frequency morphological wavelet decomposition result, including a horizontal morphological wavelet, a vertical morphological wavelet, and a diagonal morphological wavelet;
Figure 129450DEST_PATH_IMAGE083
representing a value obtained by removing a high frequency part from a high frequency morphological wavelet decomposition result;
Figure 799466DEST_PATH_IMAGE084
which represents a wavelet threshold value, is a function of,
Figure 914053DEST_PATH_IMAGE085
indicates the adjustment parameter, will
Figure 944194DEST_PATH_IMAGE085
Set to 6; wavelet decomposition result of high-frequency morphology by using threshold function
Figure 195047DEST_PATH_IMAGE086
And
Figure 35964DEST_PATH_IMAGE087
Figure 637847DEST_PATH_IMAGE088
is processed to be lower than the threshold value
Figure 973144DEST_PATH_IMAGE089
The high-frequency morphology wavelet decomposition result is set to be 0, the removal of Gaussian white noise in the image is realized, and the high-frequency morphology wavelet decomposition result after the high-frequency part is removed is obtained:
Figure 344083DEST_PATH_IMAGE032
Figure 355901DEST_PATH_IMAGE033
and reconstructing the submarine landform image according to the low-frequency morphological wavelet decomposition result and the high-frequency morphological wavelet decomposition result of the image pixel.
Meanwhile, the scheme provides a contrast enhancement method, which comprises the following steps of firstly calculating the gray average value of the submarine landform image in a logarithmic domain:
Figure 913921DEST_PATH_IMAGE045
wherein: m represents the pixel line number of the submarine relief image; n represents the pixel column number of the submarine relief image; w represents a constant, which is set to 1; setting a gray level adjustment coefficient:
Figure 302177DEST_PATH_IMAGE046
wherein:
Figure 776890DEST_PATH_IMAGE090
representing the maximum gray value of the submarine relief image;
Figure 694030DEST_PATH_IMAGE091
representing image pixels in an undersea relief image
Figure 270505DEST_PATH_IMAGE092
The gray scale adjustment coefficient of (a);
Figure 196873DEST_PATH_IMAGE093
represents a constant, which is set to 0.1; utilizing gray scale adjustment coefficient to carry out adjustment on any pixel in submarine landform image
Figure 11245DEST_PATH_IMAGE092
Carrying out gray level adjustment, wherein the gray level adjustment formula is as follows:
Figure 381178DEST_PATH_IMAGE050
wherein:
Figure 179370DEST_PATH_IMAGE094
representing an arbitrary pixel
Figure 643849DEST_PATH_IMAGE092
Adjusting image pixels in RGB color channels; the obtained image with adjusted gray scale is
Figure 843886DEST_PATH_IMAGE095
. Compared with the traditional method, the gray scale adjustment coefficient comprises the proportional relation among the current gray scale value, the average gray scale value and the maximum gray scale value of the image in a logarithmic domain, different pixels have different gray scale adjustment coefficients, so that corresponding gray scale adjustment can be realized for different gray scale levels, meanwhile, the scheme improves the traditional contrast enhancement method, and performs Gamma adjustment in the probability accumulation distribution diagram of the image gray scale, so that the gray scale value of each gray scale level is changed, and the effect of enhancing the image area information is achieved.
Drawings
Fig. 1 is a schematic flow chart of a method for denoising a submarine relief image according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
S1: morphological wavelets are constructed based on a midpoint filter in the nonlinear filter, and the removal effect on Gaussian white noise is improved.
In the step S1, a morphological wavelet is constructed based on a midpoint filter in a nonlinear filter, including:
the morphological wavelet comprises a high-frequency morphological wavelet and a low-frequency morphological wavelet, the high-frequency morphological wavelet comprises a horizontal morphological wavelet, a vertical morphological wavelet and a diagonal morphological wavelet, and the morphological wavelet based on the midpoint filter is inputThe input value being a pixel of the image
Figure 617676DEST_PATH_IMAGE001
Wherein
Figure 637585DEST_PATH_IMAGE001
The image pixel of the mth row and the nth column of the image is represented, and the filtering result of the high-frequency morphological wavelet is as follows:
Figure 171334DEST_PATH_IMAGE002
Figure 960298DEST_PATH_IMAGE003
Figure 655722DEST_PATH_IMAGE004
wherein:
Figure 179238DEST_PATH_IMAGE005
representing image pixels
Figure 985520DEST_PATH_IMAGE001
The horizontal morphological wavelet decomposition result of (1);
Figure 691308DEST_PATH_IMAGE006
representing an image pixel
Figure 908417DEST_PATH_IMAGE001
The vertical morphology wavelet decomposition result of (1);
Figure 902918DEST_PATH_IMAGE007
representing image pixels
Figure 778470DEST_PATH_IMAGE001
Diagonal ofA state wavelet decomposition result;
the filtering result of the low-frequency morphological wavelet is as follows:
Figure 542027DEST_PATH_IMAGE008
Figure 329985DEST_PATH_IMAGE009
Figure 77362DEST_PATH_IMAGE010
wherein:
Figure 491025DEST_PATH_IMAGE011
representing image pixels
Figure 374668DEST_PATH_IMAGE001
The low frequency morphological wavelet decomposition result of (1).
S2: based on the constructed morphological wavelet, performing morphological wavelet decomposition on the submarine geomorphic image to be processed to obtain a decomposed wavelet signal, and performing wavelet reconstruction on the wavelet signal after removing a high-frequency part in the wavelet signal to obtain the submarine geomorphic image after wavelet reconstruction.
In the step S2, morphological wavelet decomposition is performed on the submarine landform image to be processed based on morphological wavelets to obtain decomposed wavelet signals, including:
according to the constructed morphological wavelet, performing morphological wavelet decomposition on the seabed landform image to be processed, wherein the image pixels of any mth row and nth column in the seabed landform image to be processed are expressed as
Figure 832063DEST_PATH_IMAGE012
Using low-frequency morphological wavelets and high-frequency morphological wavelet pairs, respectively
Figure 535577DEST_PATH_IMAGE012
Performing morphological wavelet decomposition to obtain
Figure 752931DEST_PATH_IMAGE012
The low-frequency morphological wavelet decomposition result of (1):
Figure 491080DEST_PATH_IMAGE013
，
and high-frequency morphological wavelet decomposition results:
Figure 135688DEST_PATH_IMAGE014
；
wherein
Figure 77231DEST_PATH_IMAGE015
Representing image pixels
Figure 832697DEST_PATH_IMAGE012
The result of the horizontal morphological wavelet decomposition of (a),
Figure 690931DEST_PATH_IMAGE016
representing an image pixel
Figure 506441DEST_PATH_IMAGE012
As a result of the vertical-morphology wavelet decomposition of (a),
Figure 433814DEST_PATH_IMAGE017
representing an image pixel
Figure 727392DEST_PATH_IMAGE012
The diagonal form wavelet decomposition result of (1); the wavelet decomposition result is a wavelet signal obtained by decomposition;
computing image pixels
Figure 440134DEST_PATH_IMAGE012
Filter optimization value of (a):
Figure 426544DEST_PATH_IMAGE018
wherein:
Figure 857525DEST_PATH_IMAGE019
representing a median of the computed data sequence;
preserving high frequency morphological wavelet decomposition results
Figure 439948DEST_PATH_IMAGE020
The low-frequency morphological wavelet decomposition result
Figure 7195DEST_PATH_IMAGE021
As input value of morphological wavelet, decomposing to obtain
Figure 164507DEST_PATH_IMAGE021
High frequency morphological wavelet decomposition results of
Figure 551626DEST_PATH_IMAGE022
And low frequency morphological wavelet decomposition results
Figure 452586DEST_PATH_IMAGE024
。
In the step S2, wavelet reconstruction is performed on the wavelet signal after the high frequency part in the wavelet signal is removed, and the wavelet reconstruction includes:
setting a threshold function:
Figure 389187DEST_PATH_IMAGE025
wherein:
s represents a high-frequency morphological wavelet decomposition result, including a wavelet decomposition result of a horizontal morphological wavelet, a vertical morphological wavelet and a diagonal morphological wavelet;
Figure 717400DEST_PATH_IMAGE083
representing a value obtained by removing a high frequency part from a high frequency morphological wavelet decomposition result;
Figure 857394DEST_PATH_IMAGE084
which represents a wavelet threshold value, is a function of,
Figure 296466DEST_PATH_IMAGE085
indicates the adjustment parameter, will
Figure 589038DEST_PATH_IMAGE085
Set to 6; wavelet decomposition result of high-frequency morphology by using threshold function
Figure 822573DEST_PATH_IMAGE086
And
Figure 449864DEST_PATH_IMAGE087
Figure 427047DEST_PATH_IMAGE088
processing to lower than threshold
Figure 88973DEST_PATH_IMAGE089
The high-frequency morphology wavelet decomposition result is set to be 0, the removal of Gaussian white noise in the image is realized, and the high-frequency morphology wavelet decomposition result after the high-frequency part is removed is obtained:
Figure 8256DEST_PATH_IMAGE032
Figure 857263DEST_PATH_IMAGE033
reconstructing the submarine landform image according to the low-frequency morphological wavelet decomposition result and the high-frequency morphological wavelet decomposition result of the image pixel,
the submarine landform image reconstruction formula is as follows:
Figure 903717DEST_PATH_IMAGE034
Figure 154569DEST_PATH_IMAGE035
Figure 746219DEST_PATH_IMAGE036
Figure 816943DEST_PATH_IMAGE037
Figure 667087DEST_PATH_IMAGE038
wherein:
Figure 772447DEST_PATH_IMAGE039
image pixels of any mth row and nth column in the reconstructed submarine landform image are represented;
Figure 784265DEST_PATH_IMAGE040
representing image pixels
Figure 122711DEST_PATH_IMAGE041
The filter optimization value of (1).
S3: and (5) carrying out gray level adjustment on the seabed landform image after wavelet reconstruction by using a gray level correction method.
In the step S3, the graying processing is performed on the submarine landform image by using a grayscale correction method, and includes:
in an embodiment of the present invention, the gray scale correction method includes an image graying process and a gray scale adjustment; the grayscale processing flow of the submarine landform image comprises the following steps:
for any pixel in the reconstructed submarine landform image
Figure 245388DEST_PATH_IMAGE039
The maximum value of the three color channel components is calculated, the maximum value is set as the gray value of the pixel point, and the gray of the reconstructed submarine landform image is obtainedThe degree graph has the following formula of graying treatment:
Figure 736412DEST_PATH_IMAGE042
wherein:
Figure 919132DEST_PATH_IMAGE096
representing the image pixels of the nth row of any mth line in the reconstructed submarine relief image;
Figure 980760DEST_PATH_IMAGE097
are respectively pixel points
Figure 907128DEST_PATH_IMAGE096
Values in the three color channels R, G, B;
Figure 987079DEST_PATH_IMAGE098
is a pixel point
Figure 340700DEST_PATH_IMAGE096
The gray value of (a).
In the step S3, the grayscale adjustment is performed on the submarine landform image by using a grayscale correction method, including:
calculating the gray average value of the submarine landform image in a logarithmic domain:
Figure 388159DEST_PATH_IMAGE045
wherein:
m represents the pixel line number of the submarine relief image;
n represents the pixel column number of the submarine relief image;
w represents a constant, which is set to 1;
setting a gray level adjustment coefficient:
Figure 118218DEST_PATH_IMAGE046
wherein:
Figure 318255DEST_PATH_IMAGE099
represents a constant, which is set to 0.1;
Figure 842777DEST_PATH_IMAGE100
representing the maximum gray value of the submarine relief image;
Figure 862686DEST_PATH_IMAGE101
representing image pixels in an undersea relief image
Figure 147168DEST_PATH_IMAGE096
The gray scale adjustment coefficient of (a);
utilizing gray scale adjustment coefficient to carry out adjustment on any pixel in submarine landform image
Figure 201712DEST_PATH_IMAGE096
Carrying out gray level adjustment, wherein the gray level adjustment formula is as follows:
Figure 897135DEST_PATH_IMAGE050
wherein:
Figure 669919DEST_PATH_IMAGE102
representing an arbitrary pixel
Figure 725469DEST_PATH_IMAGE096
Adjusting image pixels in RGB color channels; the resulting gray-scaled image is I.
S4: and performing contrast enhancement on the submarine landform image after the gray level correction based on an improved Gamma adjustment algorithm to obtain the submarine landform image after the final denoising treatment.
In the step S4, contrast enhancement is performed on the submarine relief image based on the improved Gamma adjustment algorithm, including:
the improved Gamma adjustment algorithm flow comprises the following steps:
1) Dividing the submarine landform image I into Q continuous and non-overlapping sub-regions;
2) Calculating a gray level histogram of each subregion;
3) Calculate the average number of pixels per sub-region:
Figure 634519DEST_PATH_IMAGE053
wherein:
num represents the total number of pixels in the sub-region;
l represents the number of gray levels of the sub-region;
set the enhancement threshold to
Figure 766423DEST_PATH_IMAGE054
Making the gray level histogram of the subregion larger than
Figure 760924DEST_PATH_IMAGE054
Truncating the number of pixels, adding the truncated number of pixels, and averagely distributing the sum to each gray level;
4) Calculating the probability of different gray level pixels in the gray level histogram of each subarea after pixel distribution:
Figure 370897DEST_PATH_IMAGE055
wherein:
i represents a gray level, and L gray levels are total;
Figure 885186DEST_PATH_IMAGE056
representing the number of pixels with the gray level i in the sub-area;
num denotes the total number of pixels in the sub-region;
Figure 453570DEST_PATH_IMAGE057
a probability of occurrence of a pixel representing a gray level i in the sub-region;
the probability accumulation distribution graph PD of the sub-region gray scale is
Figure 669788DEST_PATH_IMAGE058
(ii) a Performing Gamma adjustment on the probability accumulation distribution map PD of each sub-region, and obtaining the adjusted probability accumulation distribution map
Figure 83452DEST_PATH_IMAGE059
Comprises the following steps:
Figure 216362DEST_PATH_IMAGE060
wherein:
Figure 690068DEST_PATH_IMAGE103
represents a Gamma value, which is set to 0.3;
5) And combining the sub-region images after Gamma adjustment to obtain the final denoised submarine landform image.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of another identical element in a process, apparatus, article, or method comprising the element.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (6)

1. A method for denoising a submarine relief image, which is characterized by comprising the following steps:
s1: a morphological wavelet is constructed based on a midpoint filter in a nonlinear filter, so that the removal effect of Gaussian white noise is improved;
s2: based on the constructed morphological wavelet, performing morphological wavelet decomposition on the submarine landform image to be processed to obtain a decomposed wavelet signal, and performing wavelet reconstruction on the wavelet signal after removing a high-frequency part in the wavelet signal to obtain a submarine landform image after wavelet reconstruction;
s3: carrying out gray level adjustment on the seabed landform image after wavelet reconstruction by using a gray level correction method;
s4: performing contrast enhancement on the seabed landform image subjected to gray level correction based on an improved Gamma adjustment algorithm to obtain a seabed landform image subjected to final denoising treatment;
wherein, in the step S1, the construction of the morphological wavelet based on the midpoint filter in the nonlinear filter includes:
the morphological wavelet includes a high frequency morphological wavelet and a low frequency morphological wavelet, the high frequency morphological wavelet including a horizontal waveletMorphological wavelet, vertical morphological wavelet and diagonal morphological wavelet, the morphological wavelet input value based on the midpoint filter being image pixel
Figure 783199DEST_PATH_IMAGE001
In which
Figure 169181DEST_PATH_IMAGE001
The image pixel of the mth row and the nth column of the image is represented, and the filtering result of the high-frequency morphological wavelet is as follows:
Figure 417760DEST_PATH_IMAGE002
Figure 531079DEST_PATH_IMAGE004
Figure 79872DEST_PATH_IMAGE006
wherein:
Figure 371176DEST_PATH_IMAGE007
representing an image pixel
Figure 107050DEST_PATH_IMAGE001
The horizontal morphological wavelet decomposition result of (3);
Figure 40371DEST_PATH_IMAGE008
representing an image pixel
Figure 427359DEST_PATH_IMAGE001
The vertical morphology wavelet decomposition result of (1);
Figure 889565DEST_PATH_IMAGE009
representing an image pixel
Figure 378315DEST_PATH_IMAGE001
The diagonal form wavelet decomposition result of (1);
the filtering result of the low-frequency morphological wavelet is as follows:
Figure 584168DEST_PATH_IMAGE010
Figure 576395DEST_PATH_IMAGE012
Figure 989928DEST_PATH_IMAGE014
wherein:
Figure 700395DEST_PATH_IMAGE015
representing image pixels
Figure 444360DEST_PATH_IMAGE001
The low frequency morphological wavelet decomposition result of (1).
2. The method for denoising the submarine relief image according to claim 1, wherein the step S2 of performing morphological wavelet decomposition on the submarine relief image to be processed based on morphological wavelets to obtain decomposed wavelet signals comprises:
according to the constructed morphological wavelet, performing morphological wavelet decomposition on the seabed landform image to be processed, wherein the image pixels of any mth row and nth column in the seabed landform image to be processed are expressed as
Figure 556672DEST_PATH_IMAGE016
Using low-frequency morphological wavelets and high-frequency morphological wavelet pairs, respectively
Figure 626259DEST_PATH_IMAGE016
Performing morphological wavelet decomposition to obtain
Figure 73290DEST_PATH_IMAGE016
Low frequency morphological wavelet decomposition results of
Figure 620946DEST_PATH_IMAGE017
And high frequency morphological wavelet decomposition results
Figure 853344DEST_PATH_IMAGE019
Wherein
Figure 828254DEST_PATH_IMAGE020
Representing image pixels
Figure 762580DEST_PATH_IMAGE016
The result of the horizontal morphological wavelet decomposition of (a),
Figure 113927DEST_PATH_IMAGE021
representing image pixels
Figure 200832DEST_PATH_IMAGE016
As a result of the vertical-morphology wavelet decomposition of (a),
Figure 612222DEST_PATH_IMAGE022
representing an image pixel
Figure 518998DEST_PATH_IMAGE016
The wavelet decomposition result of the diagonal line state is a wavelet signal obtained by decomposition;
computing image pixels
Figure 657724DEST_PATH_IMAGE016
Filter optimization value of (a):
Figure 599135DEST_PATH_IMAGE024
wherein:
Figure 447006DEST_PATH_IMAGE025
representing a median of the calculated data sequence;
preserving high frequency morphological wavelet decomposition results
Figure 841078DEST_PATH_IMAGE026
The low-frequency morphological wavelet decomposition result
Figure 783495DEST_PATH_IMAGE027
As input value of morphological wavelet, decomposing to obtain
Figure 579413DEST_PATH_IMAGE027
High frequency morphological wavelet decomposition results of
Figure 332605DEST_PATH_IMAGE028
And low frequency morphological wavelet decomposition results
Figure 213973DEST_PATH_IMAGE029
。
3. The method for denoising a submarine relief image according to claim 2, wherein the wavelet reconstruction is performed on the wavelet signal after removing high-frequency parts in the wavelet signal in the step S2, and includes:
setting a threshold function:
Figure 710814DEST_PATH_IMAGE030
wherein:
s represents a high frequency morphological wavelet decomposition result, including a wavelet of a horizontal morphological wavelet, a wavelet of a vertical morphological wavelet, and a wavelet of a diagonal morphological waveletDecomposing the result;
Figure 876085DEST_PATH_IMAGE031
representing a value obtained by removing a high frequency part from a high frequency morphological wavelet decomposition result;
Figure 534599DEST_PATH_IMAGE032
which represents a wavelet threshold value, is a function of,
Figure 903264DEST_PATH_IMAGE033
indicates the adjustment parameter to
Figure 938216DEST_PATH_IMAGE033
Is set to be 6;
wavelet decomposition result of high-frequency morphology by using threshold function
Figure 957993DEST_PATH_IMAGE034
And
Figure 787409DEST_PATH_IMAGE035
Figure 643369DEST_PATH_IMAGE036
is processed to be lower than the threshold value
Figure 747592DEST_PATH_IMAGE037
The high-frequency morphological wavelet decomposition result is set to be 0, and the high-frequency morphological wavelet decomposition result after the high-frequency part is removed is obtained:
Figure 372608DEST_PATH_IMAGE038
Figure 911209DEST_PATH_IMAGE039
reconstructing the seabed landform image according to the low-frequency morphological wavelet decomposition result and the high-frequency morphological wavelet decomposition result of the image pixel, wherein the reconstruction formula of the seabed landform image is as follows:
Figure 988887DEST_PATH_IMAGE040
Figure 631221DEST_PATH_IMAGE041
Figure 376323DEST_PATH_IMAGE042
Figure 813120DEST_PATH_IMAGE043
Figure 627362DEST_PATH_IMAGE044
wherein:
Figure 73386DEST_PATH_IMAGE045
representing the image pixels of the nth row of any mth line in the reconstructed submarine relief image;
Figure 407416DEST_PATH_IMAGE046
representing an image pixel
Figure 15115DEST_PATH_IMAGE047
The filter optimization value of (1).
4. The method for denoising the submarine relief image according to claim 1, wherein the graying processing of the submarine relief image by the grayscale correction method in the step S3 comprises:
the grayscale processing flow of the submarine landform image comprises the following steps:
solving the maximum value of three color channel components of any pixel in the reconstructed submarine relief image, setting the maximum value as the gray value of the pixel point, and obtaining the gray map of the reconstructed submarine relief image, wherein the formula of the graying treatment is as follows:
Figure 316652DEST_PATH_IMAGE048
wherein:
Figure 35209DEST_PATH_IMAGE045
representing the image pixels of the nth row of any mth line in the reconstructed submarine relief image;
Figure 489324DEST_PATH_IMAGE049
are respectively pixel points
Figure 267924DEST_PATH_IMAGE045
Values in the three color channels R, G, B;
Figure 73069DEST_PATH_IMAGE050
is a pixel point
Figure 844585DEST_PATH_IMAGE045
The gray value of (a).
5. The method for denoising the submarine relief image according to claim 4, wherein the step S3 of performing gray scale adjustment on the submarine relief image by using a gray scale correction method comprises:
calculating the gray average value of the submarine landform image in a logarithmic domain:
Figure 418786DEST_PATH_IMAGE051
wherein:
m represents the pixel line number of the submarine relief image;
n represents the pixel column number of the submarine relief image;
w represents a constant, which is set to 1;
setting a gray level adjustment coefficient:
Figure 368287DEST_PATH_IMAGE052
wherein:
Figure 129570DEST_PATH_IMAGE053
represents a constant, which is set to 0.1;
Figure 721088DEST_PATH_IMAGE054
representing the maximum gray value of the submarine relief image;
Figure 133484DEST_PATH_IMAGE055
representing image pixels in an undersea relief image
Figure 253887DEST_PATH_IMAGE045
The gray scale adjustment coefficient of (a);
utilizing gray scale adjustment coefficient to carry out adjustment on any pixel in submarine landform image
Figure 768045DEST_PATH_IMAGE045
Carrying out gray level adjustment, wherein the gray level adjustment formula is as follows:
Figure 632096DEST_PATH_IMAGE056
wherein:
Figure 164577DEST_PATH_IMAGE057
representing an arbitrary pixel
Figure 721460DEST_PATH_IMAGE045
Adjusting image pixels in RGB color channels; the resulting gray-scaled image is I.
6. The method for denoising a marine relief image according to claim 1, wherein the step S4 of performing contrast enhancement on the marine relief image based on the improved Gamma adjustment algorithm comprises:
the improved Gamma adjustment algorithm flow comprises the following steps:
1) Dividing the submarine landform image I into Q continuous and non-overlapping subregions;
2) Calculating a gray level histogram of each subregion;
3) Calculate the average number of pixels per sub-region:
Figure 457335DEST_PATH_IMAGE058
wherein:
num represents the total number of pixels in the sub-region;
l represents the number of gray levels of the sub-region;
set the enhancement threshold to
Figure 125077DEST_PATH_IMAGE059
Making the gray level histogram of the subregion larger than
Figure 246485DEST_PATH_IMAGE059
Pixel number section ofCutting off, adding the cut-off pixel numbers, and averagely distributing the pixel numbers to each gray level;
4) Calculating the probability of different gray level pixels in the gray level histogram of each subarea after pixel distribution:
Figure 974270DEST_PATH_IMAGE060
wherein:
i represents a gray level, and L gray levels are total;
Figure 463020DEST_PATH_IMAGE061
representing the number of pixels with the gray level i in the sub-area;
num denotes the total number of pixels in the sub-region;
Figure 668874DEST_PATH_IMAGE062
a probability of occurrence of a pixel representing a gray level i in the sub-region;
the probability accumulation distribution graph PD of the sub-region gray scale is
Figure 661100DEST_PATH_IMAGE063
(ii) a Performing Gamma adjustment on the probability accumulation distribution map PD of each sub-region, and obtaining the adjusted probability accumulation distribution map
Figure 74633DEST_PATH_IMAGE064
Comprises the following steps:
Figure 519521DEST_PATH_IMAGE065
wherein:
Figure 529065DEST_PATH_IMAGE066
represents a Gamma value, which is set to 0.3;
5) And combining the sub-region images after Gamma adjustment to obtain the final denoised submarine landform image.
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