CN113222833B - Side-scan sonar image processing method and device - Google Patents
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Abstract
The invention discloses a side-scan sonar image processing method and device, and the method comprises the following steps: 1) setting a sliding window M for the acquired side scan sonar image to perform sliding caching; 2) denoising the cached image; 3) enhancing the image in a layering way; taking the denoised image as an input signal of bilateral filtering layering, calculating high-frequency image data of single-line frame data based on cache block filtering data, and performing weighted fusion on the high-frequency image and an original data image to realize image enhancement; 4) and carrying out gray level equalization processing on the enhanced image. The method effectively enhances the details of the image through processing, and ensures the real-time performance of the processing.
Description
Technical Field
The invention relates to an image processing technology, in particular to a side scan sonar image processing method and device.
Background
The side scan sonar is an effective detection device of an underwater detection sonar, and mainly comprises a system device which transmits sound wave signals through an acoustic transducer and collects scattered echo signals to be further processed to form underwater imaging. Therefore, an effective side-scan sonar image real-time processing method is researched, and effective assistance is realized for accelerating side-scan sonar image processing and underwater acoustic image processing engineering application
Disclosure of Invention
The invention aims to solve the technical problem of providing a side-scan sonar image processing method and device aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: a side scan sonar image processing method comprises the following steps:
1) caching the acquired side-scan sonar image;
performing sliding buffer on input images according to line frames: the sliding window should be set to an odd value M not smaller than the wavelet coefficient length according to the wavelet coefficient length, and should not be set too large to ensure the processing speed.
2) Denoising the cached image;
3) enhancing the image in a layering way;
the image after denoising is used as an input signal of bilateral filtering layering, high-frequency image information of single-line frame data is calculated based on cache block filtering data, and the high-frequency image and an original data image are subjected to weighted fusion to realize image enhancement, wherein the method specifically comprises the following steps:
3.1) taking the length M of the sliding window in the step 1) as the size of the bilateral filtering blocks, and only processing ceil (M/2) th line data to obtain high-frequency image information of the single-line frame data; wherein the ceil () function is an upgoing integer rounding function;
3.2) weighting and fusing the high-frequency image and the original image data to realize image enhancement;
4) and (5) gray level equalization processing.
According to the scheme, the denoising processing in the step 2) comprises the steps of performing wavelet transformation on the stored image, denoising the image by using soft threshold filtering, and reconstructing the image by using wavelet inverse transformation.
According to the scheme, db 3-order wavelets are adopted in wavelet transformation in the step 2).
According to the scheme, the weighting coefficients adopted by the bilateral filtering in the step 3.1) are as follows:
W(x,y)＝s(x,y)*g(x,y)
wherein s (x, y) is a spatial weighting coefficient, g (x, y) is a luminance difference weighting coefficient, W (x, y) is a bilateral filtering weighting coefficient, (x, y) is an image position coordinatec,yc) As coordinates of the image center position, σdIs the spatial Gaussian standard deviation, σrIs the luminance gaussian standard deviation.
According to the scheme, the step 4) is as follows:
4.1) determining a proper sliding cache window according to the gray level distribution characteristics of the image, taking 20ping data with length as cache according to the empirical value, and storing the enhanced image data;
4.2) the collected image gray distribution based on the Gaussian model, the mean value and the variance of the enhanced image data are counted, and an effective value delta t is calculated by using a Gaussian confidence a value;
and 4.3) carrying out linear compression processing on the enhanced data according to the effective value delta t to form image data in an 8-bit standard image format.
According to the method, the side scan sonar image processing device comprises the following steps:
the cache module is used for performing sliding cache on the acquired side-scan sonar images according to line frames:
the denoising module is used for denoising the cached image;
the image layered enhancement module is used for enhancing the image of the denoised image; the method comprises the following steps: taking the denoised image as an input signal of bilateral filtering layering, calculating a high-frequency image and a low-frequency image of single-line frame data based on cache block filtering data, and performing weighted fusion on the high-frequency image and an original data image to realize image enhancement, wherein the specific steps are as follows:
1) taking the length M of a sliding window in a cache module as the size of bilateral filtering blocks, and only processing ceil (M/2) th line data to obtain high-frequency image and low-frequency image information of the single-line frame data;
2) weighting and fusing the high-frequency image and the original data image to realize image enhancement;
and the balancing module is used for carrying out gray level balancing processing on the enhanced image.
According to the scheme, the denoising processing in the denoising module comprises the steps of performing wavelet transformation on the stored image, denoising the image by using soft threshold filtering, and reconstructing the image by using wavelet inverse transformation.
According to the scheme, the wavelet transformation in the denoising module adopts db3 order wavelet.
According to the scheme, the weighting coefficients adopted by bilateral filtering in the image layering enhancement module are as follows:
W(x，y)＝s(x，y)*g(x，y)
wherein s (x, y) is a spatial weighting coefficient, g (x, y) is a luminance difference weighting coefficient, W (x, y) is a bilateral filtering weighting coefficient, (x, y) is an image position coordinatec，yc) As coordinates of the image center position, σdIs the spatial Gaussian standard deviation, σrIs the luminance gaussian standard deviation.
According to the above scheme, in the equalization module, the gray level equalization processing is performed on the enhanced image, which specifically comprises the following steps:
1) determining a proper sliding cache window according to the gray level distribution characteristics of the image, taking 20ping data with length as cache according to the empirical value, and storing the enhanced image data;
2) the method comprises the steps of collecting image gray distribution based on a Gaussian model, counting the mean value and variance of enhanced image data, and calculating an effective value delta t by using a Gaussian confidence a value;
3) and according to the effective value delta t, carrying out linear compression processing on the enhanced data to form image data in an 8-bit standard image format.
The invention has the following beneficial effects:
1. according to the invention, through a sliding cache window with reasonable design, data are rapidly and effectively extracted for two-dimensional image denoising, single-line frame data are enhanced by using two-dimensional bilateral filtering, and a bilateral filtering function is used as a filtering function for image layering, so that the problem of image edge blurring after Gaussian filtering is avoided, a detail layer obtained after filtering is relatively fine, and bilateral filtering can accelerate real-time processing of a side-scanning image, thereby effectively ensuring the real-time property of data processing;
2. the invention designs a reasonable sliding cache window, realizes the smooth processing of image Gaussian model parameters by using the Kalman filtering technology, and realizes the balanced processing of dynamic side scan sonar
3. The invention utilizes the wavelet de-noising image to carry out image enhancement, so that the high-frequency component in the image layering is relatively smooth, the details of the image are effectively enhanced, and excessive noise is not increased.
4. The invention utilizes the image gray scale statistical model to realize that sonar image data with large dynamic range is linearly compressed in a single-line frame mode and converted into standard image data suitable for human eye visual observation and display.
5. The method has high stability and is suitable for side-scan image processing under different underwater acoustic environments.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a diagram illustrating a wavelet function and a scale function according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a reconstructed wavelet function and a scaling function according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a wavelet three-scale decomposition according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a wavelet single scale decomposition of an embodiment of the present invention;
FIG. 6 is a schematic diagram of wavelet inverse transform reconstruction according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of an image processing process according to an embodiment of the invention;
fig. 8 is a graph of image processing contrast effect according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following 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.
As shown in fig. 1, a side scan sonar image processing method includes the following steps:
1) caching the acquired side-scan sonar image;
performing sliding buffering on an input image according to line frames: according to the wavelet coefficient length, the sliding window is set to be an odd value M which is not less than the wavelet coefficient length in the denoising treatment, and in addition, the sliding window is not suitable to be set to be too large in order to ensure the treatment speed.
2) Denoising the cached image;
the image is subjected to denoising processing, a large amount of scattered point noise is removed, the detail information of the image is completely reserved, and noise possibly introduced by image enhancement is reduced.
The denoising processing comprises the steps of performing wavelet transformation on a stored image, denoising the image by using soft threshold filtering, and reconstructing the image by using wavelet inverse transformation, wherein the steps are as follows:
2.1) wavelet transformation
In this embodiment, the wavelet function adopted by wavelet transform is Daubechies 3-order wavelet (referred to as Db3), the Db3 wavelet function and the scale function are shown in fig. 2, and the single-scale filter bank shown in fig. 5 can generate P-scale transform in the similar scale J-1, J-2, … and J-P in an "iteration" manner, where P in the method is 3, and the original image is taken as the original image
Input of, its row and
sum wavelet function ahψThe (-n) convolution is performed and downsampled to obtain two sub-images. Then, the same filtering process is performed on the two sub-image information in the column direction and down-sampled. Four sub-images including three high-frequency component images and one low-frequency component image are obtained through single-scale transformation, the low-frequency component image is subjected to single-scale decomposition again, the steps are repeated, and three-scale decomposition is carried out in total as shown in fig. 4.
2.2) Soft threshold Filtering
Performing soft threshold filtering processing on all the obtained high-frequency coefficients, wherein the adopted threshold algorithm is a Visushrink threshold, and the threshold is a Visushrink threshold
Sigma is the standard deviation of the noise, N is the number of image pixels, and according to an empirical formula, the standard deviation sigma value of the noise is the product of the median value of the input image and 1/0.6745;
2.3) image reconstruction
Performing wavelet inverse transformation on the filtered high-frequency coefficient and the filtered low-frequency coefficient, as shown in fig. 6, wherein a wavelet function and a scale function of the inverse transformation are the order inversions of the wavelet function and the scale function in the wavelet transformation, as shown in fig. 3;
image reconstruction, four images are upsampled and convolved with two one-dimensional filters in each iteration and decomposed into the inverse transform with the wavelet. Adding the results to obtain an approximation with a dimension of j +1, and performing iterative processing until the original image is restored
And completing image reconstruction.
3) Enhancing the image in a layering way;
taking the denoised image as an input signal of bilateral filtering layering, calculating high-frequency image information of single-line frame data based on cache block filtering data, and performing weighted fusion on the high-frequency image and an original data image to realize image enhancement, wherein a processing result is shown in fig. 7 and specifically comprises the following steps:
3.1) taking the length M of the sliding window in the step 1) as the size of the bilateral filtering block, and only processing ceil (M/2) th line data to obtain high-frequency image and low-frequency image information of the single-line frame data;
3.2) weighting and fusing the high-frequency image and the original data image to realize image enhancement;
according to the method, the bilateral filtering function is used as the filtering function of image layering, so that the problem of image edge blurring after Gaussian filtering is avoided, the detail layer obtained after filtering is very fine, and on the other hand, bilateral filtering is easy to realize and can accelerate real-time processing of the side-scan image.
Bilateral filtering benefits from but is different from gaussian filtering, bilateral filtering considers both spatial factors and luminance difference factors, and the weighting formula is shown as follows,
W(x，y)＝s(x，y)*g(x，y) (3)
the formula (1) is a spatial weighting coefficient, the formula (2) is a luminance difference weighting coefficient, the formula (3) is a bilateral filtering weighting coefficient, wherein (x, y) is an image position coordinate, (x)c，yc) As the image center position, σdIs the spatial Gaussian standard deviation, σrIs the luminance gaussian standard deviation.
4) And (5) gray level equalization processing.
The method adopts image gray information based on Gaussian distribution to calculate the effective value of the image, utilizes the effective value to carry out linear gray level adjustment with limited amplitude on data in a large dynamic range, converts the data into a standard image suitable for human vision and display, and utilizes Kalman filtering to carry out filtering processing on statistical information so as to realize real-time equalization of the rolling side-scan image.
The method comprises the following specific steps:
4.1) determining a proper sliding cache window according to the gray level distribution characteristics of the image, taking 20ping data with length as cache according to the empirical value, and storing the enhanced image data.
4.2) the collected image gray distribution based on the Gaussian model, the mean value and the variance of the enhanced image data are counted, and the effective value delta t is calculated by using the Gaussian confidence a value.
Due to the fact that the underwater environment is complex, the dynamic range of sonar data acquisition is large, some special bright spots or dark spots can appear in an image, the spots cannot reflect real image information, an effective value delta t of the image needs to be calculated, the delta t serves as the maximum value of the image, and image data are mapped linearly. The calculation of the effective value is performed according to the gray level histogram, because the original image data is in a 16-bit or 32-bit data format, the effective value is not suitable for direct statistical histogram calculation, in this embodiment, the gaussian distribution N (μ, δ) is converted into the standard positive distribution N (0,1) as formula (4), and the value table can be queried to know α according to the effective interval probability value by using the standard positive distribution value table.
According to the experimental effect, pixels with the gray information within 99.4% are within the visual range in a default mode, namely the pixels are within the visual range when the confidence interval is greater than 99.4%. The value α is found to be about 2.5 when the standard positive space book index is used to look up a value α of 99.4%.
The value of X is calculated according to equation (6),
X＝αδ+μ (6)
the value of X is also the value of the effective value Δ t.
And 4.3) carrying out linear compression processing on the enhanced data according to the effective value delta t to form image data in an 8-bit standard image format.
The image gain value B is obtained by using delta t, as shown in formula (7),
output data value YiAs shown in formula (8),
Yi＝B×xi (8)
in the formula, xiIs the input pixel gray value.
And (3) setting a reasonable sliding cache window, taking the enhanced image as the input of gray statistics, calculating the mean value and the variance of the enhanced image, calculating an effective value X by using an equation (6), calculating a gain value B according to the effective value, and correcting and smoothing the B by using Kalman filtering. Finally, the output data is calculated using equation (8), and the data larger than 255 is set to 255. Finally, the method has the advantages that the finished result is shown in fig. 8, the dynamic range of the original image is large, the image is not beneficial to visual observation of human eyes, after mapping after manual adjustment, the image has high noise and poor contrast, the image processed by the method is remarkably improved, manual adjustment is not needed, and the image data can be automatically processed in real time.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (8)
1. A side-scan sonar image processing method is characterized by comprising the following steps:
1) setting a sliding window M for the acquired side scan sonar image to perform sliding caching;
2) denoising the cached image;
3) enhancing the image in a layering way;
taking the denoised image as an input signal of bilateral filtering layering, calculating high-frequency image data of single-line frame data based on cache block filtering data, and performing weighted fusion on the high-frequency image and an original data image to realize image enhancement, wherein the method specifically comprises the following steps:
3.1) selecting ceil (M/2) row data by taking the length M of the sliding window in the step 1) as the size of the bilateral filtering block to obtain high-frequency image information of the single-line frame data;
3.2) weighting and fusing the high-frequency image and the original image data to realize image enhancement;
4) carrying out gray level equalization processing on the enhanced image; the method comprises the following specific steps:
4.1) determining a corresponding sliding cache window according to the gray level distribution characteristics of the image, and storing the enhanced image data;
4.2) the acquired image gray distribution based on the Gaussian model is used for counting the mean value and the variance of the enhanced image data, and the effective value delta t of the image is calculated by using the Gaussian confidence a value;
the method comprises the following specific steps:
converting the image gray distribution N (mu, delta) based on the Gaussian model into a standard positive Tai distribution N (0,1) as follows
According to the gray information effective interval probability value, a standard positive-Taiwan distribution numerical table is utilized to inquire the numerical table to obtain a Gaussian confidence a value;
the value of X is calculated and,
X＝αδ+μ
the value of X is also the value of effective value delta t;
and 4.3) carrying out linear compression processing on the enhanced data according to the effective value delta t to form image data in an 8-bit standard image format.
2. The side-scan sonar image processing method according to claim 1, wherein the denoising in step 2) includes performing wavelet transform on the stored image, denoising the image by using soft threshold filtering, and reconstructing the image by using inverse wavelet transform.
3. The side-scan sonar image processing method according to claim 2, wherein the sliding window M in step 1) is set to an odd value not less than the wavelet coefficient length according to the wavelet coefficient length.
4. The side-scan sonar image processing method according to claim 1, wherein weighting coefficients used for the bilateral filtering in step 3.1) are as follows:
W(x,y)＝s(x,y)*g(x,y)
wherein s (x, y) is a spatial weighting coefficient, g (x, y) is a luminance difference weighting coefficient, W (x, y) is a bilateral filtering weighting coefficient, (x, y) is an image position coordinatec,yc) As coordinates of the image center position, σdIs the spatial Gaussian standard deviation, σrIs the luminance gaussian standard deviation.
5. A side scan sonar image processing apparatus, comprising:
the cache module is used for performing sliding cache on the acquired side-scan sonar images according to line frames:
the denoising module is used for denoising the cached image;
the image layered enhancement module is used for enhancing the image of the denoised image; the method comprises the following steps: taking the denoised image as an input signal of bilateral filtering layering, calculating high-frequency image data of single-line frame data based on cache block filtering data, and performing weighted fusion on the high-frequency image and an original data image to realize image enhancement, wherein the method specifically comprises the following steps:
1) taking the length M of a sliding window in a cache module as the size of bilateral filtering blocks, and only selecting ceil (M/2) th line data to obtain high-frequency image information of the single-line frame data;
2) weighting and fusing the high-frequency image and the original data image to realize image enhancement;
the equalization module is used for carrying out gray level equalization processing on the enhanced image, and specifically comprises the following steps:
1) determining a corresponding sliding cache window according to the gray level distribution characteristics of the image, and storing the enhanced image data;
2) the method comprises the steps of collecting image gray distribution based on a Gaussian model, counting the mean value and variance of enhanced image data, and calculating the effective value delta t of an image by using a Gaussian confidence a value;
the method comprises the following specific steps:
converting the image gray distribution N (mu, delta) based on the Gaussian model into a standard positive Tai distribution N (0,1) as follows
According to the gray information effective interval probability value, a standard positive-Taiwan distribution numerical table is utilized to inquire the numerical table to obtain a Gaussian confidence a value;
the value of X is calculated and,
X＝αδ+μ
the value of X is also the value of effective value delta t;
3) and according to the effective value delta t, carrying out linear compression processing on the enhanced data to form image data in an 8-bit standard image format.
6. The side-scan sonar image processing apparatus according to claim 5, wherein the denoising in the denoising module includes performing wavelet transform on the stored image, denoising the image by soft threshold filtering, and reconstructing the image by inverse wavelet transform.
7. The side-scan sonar image processing apparatus according to claim 5, wherein the wavelet transform in the denoising module employs a db3 order wavelet.
8. The side-scan sonar image processing apparatus according to claim 5, wherein weighting coefficients used for bilateral filtering in the image hierarchy enhancing module are as follows:
W(x,y)＝s(x,y)*g(x,y)
wherein s (x, y) is a spatial weighting coefficient, g (x, y) is a luminance difference weighting coefficient, W (x, y) is a bilateral filtering weighting coefficient, (x, y) is an image position coordinatec,yc) As coordinates of the image center position, σdIs the spatial Gaussian standard deviation, σrIs the luminance gaussian standard deviation.
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