CN109785260B - Nonlinear enhancement method for side-scan sonar image - Google Patents
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Abstract
The invention belongs to the technical field of side-scan sonar image processing, and particularly relates to a non-linear enhancement method for a side-scan sonar image. The invention comprises the following steps: the method comprises the steps of obtaining an original image from an original side-scan sonar data file, sequentially carrying out gray level normalization, neighborhood extreme value suppression and Gaussian smoothing on the original image, calculating a maximum value of a low gray level area, a minimum value of a high gray level area and a middle gray level area according to the smoothed image, and finally respectively carrying out nonlinear correction on the three areas to obtain an enhanced image. Through the steps, the method can quickly, effectively and low-cost realize the enhancement of the effective signals and the inhibition of the ineffective signals of the original side-scan sonar image, enhance the contrast of the local features of the image, keep the monotonicity of the edge and the gray level distribution of the original image, and not cause false edges.
Description
Technical Field
The invention belongs to the technical field of side-scan sonar image processing, and particularly relates to a non-linear enhancement method for a side-scan sonar image.
Background
With the development of scientific technologies of various countries in the world, exploration of marine environments and development and utilization of seabed resources are one of the key technologies for improving the comprehensive strength of various countries. Because sound waves have good transmission performance in a water medium, sonar imaging technology is widely applied to deep-sea remote detection, but because of the reasons of low contrast, edge blurring and the like of an original side-scan sonar image, the original sonar image cannot be directly used in post-processing such as feature detection, target recognition and the like, and therefore the original side-scan sonar image needs to be enhanced.
The side-scan sonar image enhancement is divided into two major categories, namely a hardware correction method and an image domain correction method. The common hardware correction method is a time-varying gain method, however, the method cannot be completely consistent with the attenuation process, sometimes even causes secondary gray scale distortion, and needs additional hardware support, which consumes cost. The image domain correction method mainly comprises a space domain-based correction method and a frequency domain-based correction method. Common spatial domain-based correction methods include an average gray scale correction method, a histogram correction method and a gamma correction method, which have the advantage of high speed, but the methods can amplify noise while enhancing gray scale, and even destroy the edge and feature distribution of the original image, resulting in poor image quality. Common frequency domain-based correction methods include Curvelet transform and wavelet transform, which, although they work well for feature enhancement, edge preservation, and noise suppression, consume a lot of time and cannot guarantee the real-time performance of post-processing. According to the imaging principle of a side scan sonar, the gray level of a side scan sonar image can be divided into a low gray level area, a middle gray level area and a high gray level area, the low gray level area is an invalid signal area which is only provided with weak noise due to the shielding of an underwater object or a submarine mountain ridge, the high gray level area is an area with stronger reflected waves, most of the middle gray level area is a reflection area of a background, and at present, a side scan sonar image enhancement method which is operated aiming at the gray level distribution characteristics of the three areas does not exist.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a non-linear enhancement method for a side-scan sonar image, which can quickly, efficiently, and inexpensively enhance an effective signal and suppress an ineffective signal of an original side-scan sonar image by using a characteristic of a side-scan sonar image gray scale distribution, and does not introduce a false edge or the like.
The purpose of the invention is realized as follows:
a non-linear enhancement method for a side scan sonar image comprises the following steps:
(1) acquiring an original image I from an original sonar data file;
(2) linear mapping of grey values in the original image I to grey levels 0,1]Within the range, a normalized image I is obtained1；
(3) For image I1Performing neighborhood extreme value inhibition to obtain an inhibited image I2；
(4) For the suppressed image I2Performing Gaussian smoothing to obtain an image I3；
(5) For image I after Gaussian smoothing3Firstly, according to the imaging principle of the side scan sonar image and the distribution characteristics of the gray histogram, the ratio of the low gray area and the high gray area to the total image pixel number is respectively calculated, and then the maximum value S of the gray in the low gray area is obtainedbHigh gray area gray minimum value TbAnd an intermediate gray region (S)b,Tb)；
(6) For image I after Gaussian smoothing3Carrying out nonlinear correction on three areas, namely a low gray area, a high gray area and a middle gray area, which are divided in the middle, carrying out gamma correction on the low gray area and the middle gray area, and carrying out proportional correction on the high gray area to obtain an enhanced image I4。
The step 3 specifically comprises the following steps:
(3.1) normalization of image I1In each pixel point I1(u, v) respectively carrying out maximum value filtering, minimum value filtering and average value filtering of 3 x 3 scales to respectively obtain an image Imax、IminAnd Imid；
u and v respectively represent the coordinates of a longitudinal axis and a horizontal axis under an image coordinate system;
(3.2) Filtering the processed image I according to the maximum valuemaxAnd the image I after the minimum value filtering processingminDetermining an image I1Maximum and minimum points in, rootImage I after filtering processing according to average valuemidAnd obtaining an extremum-suppressed image I by the following formula2。
The step 5 specifically comprises the following steps:
(5.1) smoothing the Gaussian processed image I3The horizontal axis of the gray level histogram is equally divided into 20 segments, the number of pixels in each segment is counted according to the sequence of the gray level values from small to large, and a counted number sequence H is obtainediAnd i represents the statistical number sequence HiIndex of (c), i ∈ [1,20 ]]；
(5.2) according to the statistical series HiCalculating image I after low gray area occupying Gaussian smoothing processing3The proportion l of the total number of pixels, the calculation method comprises the following steps:
(5.2.1) initialize index i ═ 1, input H1、H2And H3；
(5.2.2) if H1+H2≤0.5H3If the low-gray-scale area is a low-gray-scale area which is generated by the occlusion of the background or the target and is only a place with dark gray scale at the background in the image, l is 0;
(5.2.3) if 0.5H3＜H1+H2≤2H3Indicating that there are a small number of occluded low gray areas in the image, then l is calculated by the following formula:
(5.2.4) if H1+H2＞2H3Indicating that there are a large number of occluded low gray areas in the image, calculating l through step 5.2.5 to step 5.2.8;
(5.2.5) setting the correction coefficient b to 0.1 and the index i to 2;
(5.2.6) index i ═ i + 1;
(5.2.7) if Hi+1＜HiAnd i ≠ 19, and the correction coefficient b ≠ b +0.05, go to step 5.2.6;
(5.2.8) if Hi+1≥HiOr i ═ 19, then l is calculated from the following equation:
(5.3) the ratio l also represents the maximum value of the low gray level region on the horizontal axis of the gray level histogram, and in order to avoid enhancement failure caused by excessive calculation of the low gray level region, the maximum value of the low gray level region needs to be limited if the image I3The median of the pixel gray value is M, the maximum value S of the low gray areab＝min(l,M)；
(5.4) separately obtaining images I after Gaussian smoothing3The gray value b of the 95 th percent position according to the order of the gray values from small to large1And the gray value b of the 99.9% position2B is calculatedm＝(b1+b2)/2；
(5.5) statistical Gaussian smoothed image I3In the interval [ b ]1,bm]And interval [ b ]m,b2]Number of pixels t1And t2The minimum value T of the high gray areabCalculated from the following equation:
(5.6) Gray maximum value S according to the Low Gray regionbHigh gray area gray minimum value TbObtaining an intermediate gray scale region (S)b,Tb)。
The nonlinear correction function in step 6 is shown by the following formula:
in the formula I3Representing gamma correction of images after gaussian smoothingCoefficient, SbRepresenting the maximum value of the gray level, T, in the low gray level regionbRepresenting the minimum value of the gray level, T, in the high gray level regionpRepresenting the correction factor, k, of the high gray areas1Scale factor, k, representing low gray areas2Scale factors representing high gray areas, which satisfy the following relationship:
Tp＝min{(Tb-Sb)α+0.1,1.}
compared with the prior art, the invention has the beneficial effects that:
1. aiming at the characteristics of the gray level distribution of a side-scan sonar image, the gray level characteristics of a middle gray level area and a high gray level area of an effective signal are enhanced, meanwhile, a low gray level area of an ineffective signal is restrained, and the contrast between local characteristics is improved;
2. compared with a time-varying gain method, the method does not need additional hardware support, and saves cost;
3. compared with a spatial domain correction method, the method can effectively weaken the influence of impulse noise, maintain the monotonicity of the edge and gray distribution of the original image and do not introduce artificial features;
4. compared with a frequency domain correction method, the method is simple to operate, does not need to set extra hyper-parameters, has strong adaptability and high processing speed, and ensures the real-time requirement of post-processing.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of a method for calculating the ratio of low gray scale regions to the total number of full image pixels;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Fig. 1 is a flowchart of a non-linear enhancement method for a side scan sonar image provided by the present invention, which includes the following steps:
(1) acquiring an original image I from an original sonar data file;
(2) linear mapping of grey values in the original image I to grey levels 0,1]Within the range, a normalized image I is obtained1The results are shown in the original figures of examples 1 to 4 in Table 1.
In example 1, the image contains only a single object; example 2 shows a case where an image contains only a gentle background; example 3 shows a case where an image contains an uneven background; example 4 shows a case where a plurality of objects are included in an image;
since the side-scan sonar image is affected by the reverberation effect to cause a large amount of speckle noise in the side-scan sonar image, the operations of step 3 and step 4 are performed to weaken the speckle noise of the sonar image.
(3) For image I1Performing neighborhood extreme value inhibition to obtain an inhibited image I2；
(3.1) normalization of image I1In each pixel point I1(u, v) respectively carrying out maximum value filtering, minimum value filtering and average value filtering of 3 x 3 scales to respectively obtain an image Imax、IminAnd Imid；
u and v respectively represent the coordinates of the vertical axis and the horizontal axis in the image coordinate system.
(3.2) Filtering the processed image I according to the maximum valuemaxAnd the image I after the minimum value filtering processingminDetermining an image I1The maximum value point and the minimum value point in the image are filtered according to the average valuemidAnd obtaining an image I after extreme value suppression by the formula (1)2：
(4) For the suppressed image I2Performing Gaussian smoothing to obtain an image I3；
The gray scale of the side scan sonar image is mainly determined by the intensity of echo waves and can be divided into a low gray scale area, a middle gray scale area and a high gray scale area. The low gray scale region is an invalid signal region with only weak noise due to the shielding of underwater objects or submarine mountains, the high gray scale region is a region with stronger reflected waves, and most of the middle gray scale region is a reflected region of the background. In order to determine each gray scale region in the sonar image, the operation of step 5 is performed.
(5) For image I after Gaussian smoothing3Firstly, according to the imaging principle of the side scan sonar image and the distribution characteristics of the gray histogram, the ratio of the low gray area and the high gray area to the total image pixel number is respectively calculated, and then the maximum value S of the gray in the low gray area is obtainedbHigh gray area gray minimum value TbAnd an intermediate gray region (S)b,Tb)；
(5.1) smoothing the Gaussian processed image I3The gray histogram of (1) is shown as the gray histogram of examples 1 to 4 in table 1, the horizontal axis of the gray histogram is equally divided into 20 segments according to experience, the number of pixels in each segment is counted in the order of gray value from small to large, and the number sequence H is obtainediAnd i represents the statistical number sequence HiIndex of (c), i ∈ [1,20 ]]；
(5.2) according to the statistical series HiCalculating low gray area in image I3The ratio of the total number of pixels, i, is calculated as follows, and the flow chart is shown in fig. 2;
(5.2.1) initialize index i ═ 1, input H1、H2And H3；
(5.2.2) if H1+H2≤0.5H3The part with lower gray value in the image is only the part with darker gray at the background, and is not generated by being blocked by the seabed background or the targetIn the low gray area, l is 0;
(5.2.3) if 0.5H3＜H1+H2≤2H3Indicating that there are a small number of occluded low gray areas in the image, then l is calculated by the following formula:
(5.2.4) if H1+H2＞2H3Indicating that there are a large number of occluded low gray areas in the image, calculating l through step 5.2.5 to step 5.2.8;
(5.2.5) setting the correction coefficient b to 0.1 and the index i to 2;
(5.2.6) index i ═ i + 1;
(5.2.7) if Hi+1＜HiAnd index i ≠ 19, correction coefficient b ≠ b +0.05, go to step 5.2.6;
(5.2.8) if Hi+1≥HiOr the index i is 19, then l is calculated by the following formula:
the (5.3) ratio l also represents the maximum value of the low gray level region on the horizontal axis of the gray level histogram, and the maximum value of the low gray level region needs to be limited in order to avoid the possibility of enhancement failure due to excessive calculation of the low gray level region. If the image I3The median of the pixel gray value is M, the maximum value S of the low gray areab＝min(l,M)；
(5.4) respectively obtaining images I after Gaussian smoothing treatment according to experience3The gray value b of the 95 th percent position according to the order of the gray values from small to large1And the gray value b of the 99.9% position2B is calculatedm＝(b1+b2)/2；
(5.5) statistical Gaussian smoothed image I3In the interval [ b ]1,bm]And interval [ b ]m,b2]Of a pixelNumber t1And t2The minimum value T of the high gray areabCalculated from the following equation:
(5.6) Gray maximum value S according to the Low Gray regionbHigh gray area gray minimum value TbObtaining an intermediate gray scale region (S)b,Tb). Image I3Middle gray scale range of 0, Sb]The pixel of (a) represents the black portion of the low gray area map in table 1; image I3Middle gray scale range in [ Tb,1]The pixel (b) represents the white portion of the high gray area map in table 1; image I3The rest is the middle gray area (S)b,Tb)；
The gradation distributions of the low gradation region and the intermediate gradation region follow the gamma distribution, so gamma correction is performed for the low gradation region and the intermediate gradation region, while the gradation distribution of the high gradation region is approximately linear, so proportional correction is performed for the high gradation region. For the above reason, the operation of step 6 is performed.
(6) For image I3And carrying out nonlinear correction on three areas, namely a low gray area, a high gray area and a middle gray area which are divided in the middle. Gamma correction is carried out on the low gray area and the middle gray area, and proportional correction is carried out on the high gray area to obtain an enhanced image I4；
The nonlinear correction function is as follows:
wherein α represents a gamma correction coefficient, TpRepresenting the correction factor, k, of the high gray areas1Scale factor, k, representing low gray areas2Scale factors representing high gray areas, which satisfy the following relationship:
Tp＝min{(Tb-Sb)α+0.1,1.}
wherein, in order to restrain the image gray scale distortion caused by the overcorrection, alpha is not lower than 0.5; considering that there is a certain deviation in the above area calculation, in order to ensure monotonic continuity of the nonlinear correction function for image enhancement without destroying the gray distribution and edge distribution characteristics of the original image, it is required that the maximum gray value of the low gray area in the corrected image should be equal to the minimum gray value of the intermediate gray area, and the maximum gray value of the intermediate gray area should be equal to the minimum gray value of the high gray area, and the results are shown in the enhanced images of examples 1 to 4 in table 1.
TABLE 1
In order to demonstrate the advantages of the present invention, the average gray scale correction method, the histogram correction method, and the gamma correction method, the wavelet transform method, and the method of the present invention will be verified in terms of the operation mode, the processing time, the image retention, and the contrast enhancement.
In terms of operation, the process of the invention compares the results of the reactions of the articles Arici, T, Dikbas, S,&Altunbasak,Y.A histogram modification framework and its application for image contrast enhancement[J]IEEE Transactions on Image Processing,2009,18(9),1921-]Harbin engineering university 2015:9-26. gamma correction method, article Pryadharsini, R at al.A. wave transform based contrast enhancement method for underwater acoustic images[J]The wavelet transformation method has the advantage of not needing to additionally set the size of parameters such as windows, scales, coefficients and the like, and the internal relevant parameters of the region determination and the nonlinear correction function are determined according to the image I3The gray distribution characteristics of the image are self-adaptively adjusted.
The method of the invention and methods based on time domain correction such as histogram correction are fast in terms of processing time, as shown in table 2, where the method maintains the time consumption of enhancement of the images in table 1 examples 1-4 at around 20 ms. The time consumed by the wavelet transform method in the image processing for 200 pixels × 300 pixels is more than 1s, which means that a large amount of time is consumed only in the preprocessing step, which is not favorable for the real-time performance of target segmentation and detection in the later stage, so the wavelet transform method is not discussed later.
TABLE 2
Unit: ms is
In the aspect of image retention, indexes such as peak signal-to-noise ratio (PSNR) and Structural Similarity (SSIM) are used for verification.
For image I4And (5) performing edge-preserving filtering with the radius of 3 and the normalization coefficient of 0.64 to obtain a reference image K. Among them, the Hold edge Filter citation article Kou F, Chen W, Wen C, et al].IEEE Transactions on Image Processing,2015,24(11):1-1。
The peak signal-to-noise ratio PSNR is calculated by the following formula:
where MAX is the maximum value of the image pixel gray scale; MSE is the mean square error, calculated from the following equation:
in the formula, m and n are images I4And the pixel size of image K.
The structural similarity SSIM is calculated by the following formula:
in the formula, muIAnd muKRespectively representing images I4And the mean value of the K gray levels of the image; sigmaIAnd σKRespectively representing images I4And variance of K gray of the image; sigmaIKRepresenting an image I4And the covariance between images K; c1And C2Is constant in order to avoid the denominator being 0 and maintain stability, and C is usually selected1＝0.0001，C2＝0.0009。
Table 3 shows the peak snr and structural similarity evaluation indices of the enhanced images of examples 1-4 in table 1 using the average gray scale correction method, histogram correction method and gamma correction method and the method of the present invention. As can be seen from Table 3, the peak signal-to-noise ratio and the structural similarity index of the enhanced images of examples 1 to 4 in Table 1 are all higher than those of the other three methods, which shows that the method of the present invention not only has the capability of suppressing impulse noise, but also has good feature retention capability. The peak signal-to-noise ratio of the image processed by the histogram correction method is the lowest, and the structural similarity is the worst, so that the method will not be discussed later.
TABLE 3
In contrast enhancement, the distance between every two gray scale regions among the low gray scale region, the middle gray scale region and the high gray scale region can be calculated to serve as an evaluation index of contrast. The absolute values LM between the average value of the low-gray-scale area gray scales and the average value of the intermediate-gray-scale area gray scales, MH between the average value of the intermediate-gray-scale area gray scales and the average value of the high-gray-scale area gray scales, and LH between the average value of the low-gray-scale area gray scales and the average value of the high-gray-scale area gray scales of the original image, the image after the average gray scale correction method, the image after the gamma correction method, and the image after the processing by the method of the present invention are calculated, respectively, as shown in table 4. Since example 2 in table 1 does not contain a distinct low gray region and a high gray region, it will not be discussed. As can be seen from table 4, the method of the present invention improves three indicators, namely LM, MH, and LH, of the original image and the image processed by the average gray scale correction method and the gamma correction method, and particularly, the improvement of LM and LH is more obvious, which indicates that the method of the present invention has a good contrast enhancement effect.
TABLE 4
Claims (3)
1. A non-linear enhancement method for a side scan sonar image is characterized by comprising the following steps:
(1) acquiring an original image I from an original sonar data file;
(2) linear mapping of grey values in the original image I to grey levels 0,1]Within the range, a normalized image I is obtained1；
(3) For image I1Performing neighborhood extreme value inhibition to obtain an inhibited image I2；
(4) For the suppressed image I2Performing Gaussian smoothing to obtain an image I3；
(5) For image I after Gaussian smoothing3Firstly, according to the imaging principle of the side scan sonar image and the distribution characteristics of the gray level histogram, respectively calculating the low gray level area and the high gray level areaThe ratio of the gray area to the whole image pixel number is calculated, and then the maximum value S of the gray in the low gray area is calculatedbHigh gray area gray minimum value TbAnd an intermediate gray region (S)b,Tb)；
(5.1) smoothing the Gaussian processed image I3The horizontal axis of the gray level histogram is equally divided into 20 segments, the number of pixels in each segment is counted according to the sequence of the gray level values from small to large, and a counted number sequence H is obtainediAnd i represents the statistical number sequence HiIndex of (c), i ∈ [1,20 ]]；
(5.2) according to the statistical series HiCalculating image I after low gray area occupying Gaussian smoothing processing3The proportion of the total number of pixels l;
(5.2.1) initialize index i ═ 1, input H1、H2And H3；
(5.2.2) if H1+H2≤0.5H3If the low-gray-scale area is a low-gray-scale area which is generated by the occlusion of the background or the target and is only a place with dark gray scale at the background in the image, l is 0;
(5.2.3) if 0.5H3＜H1+H2≤2H3Indicating that there are a small number of occluded low gray areas in the image, then l is calculated by the following formula:
(5.2.4) if H1+H2＞2H3Indicating that there are a large number of occluded low gray areas in the image, calculating l through step 5.2.5 to step 5.2.8;
(5.2.5) let the correction coefficient b =0.1, index i = 2;
(5.2.6) index i = i + 1;
(5.2.7) if Hi+1＜HiAnd i ≠ 19, and the correction coefficient b ≠ b +0.05, go to step 5.2.6;
(5.2.8) if Hi+1≥HiOr i ═ 19, then l is calculated from the following equation:
(5.3) the ratio l also represents the maximum value of the low gray level region on the horizontal axis of the gray level histogram, and in order to avoid enhancement failure caused by excessive calculation of the low gray level region, the maximum value of the low gray level region needs to be limited if the image I3The median of the pixel gray value is M, the maximum value S of the low gray areab＝min(l,M)；
(5.4) separately obtaining images I after Gaussian smoothing3The gray value b of the 95 th percent position according to the order of the gray values from small to large1And the gray value b of the 99.9% position2B is calculatedm＝(b1+b2)/2；
(5.5) statistical Gaussian smoothed image I3In the interval [ b ]1,bm]And interval [ b ]m,b2]Number of pixels t1And t2The minimum value T of the high gray areabCalculated from the following equation:
(5.6) Gray maximum value S according to the Low Gray regionbHigh gray area gray minimum value TbObtaining an intermediate gray scale region (S)b,Tb)；
(6) For image I after Gaussian smoothing3Carrying out nonlinear correction on three areas, namely a low gray area, a high gray area and a middle gray area, which are divided in the middle, carrying out gamma correction on the low gray area and the middle gray area, and carrying out proportional correction on the high gray area to obtain an enhanced image I4。
2. The method for enhancing the nonlinearity of the side-scan sonar image according to claim 1, wherein step 3 specifically comprises the following steps:
(3.1) normalization of image I1In each pixel point I1(u, v) respectively carrying out maximum value filtering, minimum value filtering and average value filtering of 3 x 3 scales to respectively obtain an image Imax、IminAnd Imid；
u and v respectively represent the coordinates of a longitudinal axis and a horizontal axis under an image coordinate system;
(3.2) Filtering the processed image I according to the maximum valuemaxAnd the image I after the minimum value filtering processingminDetermining an image I1The maximum value point and the minimum value point in the image are filtered according to the average valuemidAnd obtaining an extremum-suppressed image I by the following formula2；
3. The method for enhancing the nonlinearity of a side-scan sonar image according to claim 1, wherein the nonlinear correction function in step 6 is represented by the following formula:
in the formula I3(u, v) represents the gamma correction coefficient of the image after the Gaussian smoothing, SbRepresenting the maximum value of the gray level, T, in the low gray level regionbRepresenting the minimum value of the gray level, T, in the high gray level regionpRepresenting the correction factor, k, of the high gray areas1Scale factor, k, representing low gray areas2Scale factors representing high gray areas, which satisfy the following relationship:
Tp＝min{(Tb-Sb)α+0.1,1.}
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794689A (en) * | 2015-03-12 | 2015-07-22 | 哈尔滨工程大学 | Preprocessing method for enhancing sonar image contract |
| CN109102479A (en) * | 2018-06-29 | 2018-12-28 | 中国船舶重工集团公司第七〇五研究所 | A kind of sonar target Enhancement Method of new images operator |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794689A (en) * | 2015-03-12 | 2015-07-22 | 哈尔滨工程大学 | Preprocessing method for enhancing sonar image contract |
| CN109102479A (en) * | 2018-06-29 | 2018-12-28 | 中国船舶重工集团公司第七〇五研究所 | A kind of sonar target Enhancement Method of new images operator |
Non-Patent Citations (2)
| Title |
|---|
| Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain;Yan Zhou et al.;《Mathematical Problems in Engineering》;20150407;全文 * |
| 侧扫声呐图像特征匹配方法研究;李鹏;《中国博士学位论文全文数据库 信息科技辑》;20180615;正文第20-22页 * |
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