A method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm

Abstract

The present invention proposes a method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm, using an underwater image acquisition device to collect images of a plane illuminated by a line laser underwater; processing the images, removing out-of-focus particles in water, and obtaining focused sediment particle distribution information. The present invention can remove out-of-focus particles accurately and quickly through line laser chip selection combined with a focusing algorithm.

Description

Technical Field

[0001] The invention belongs to the field of hydraulic sediment model detection, and specifically is a method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm. Background Art

[0002] Particles are everywhere around us. They exist in people's daily production and life. They are widely used in industry, agriculture, pharmaceutical industry and natural environment. With the development of society, people in these fields are eager to understand more characteristics of particles. For example, in the industrial production process (thermal power generation, steel mills, chemical plants), there will be a large amount of emissions (including tail gas, wastewater, residues, etc.), which pose a serious threat to the environment. People need to understand the particles in these emissions; in agricultural production, people need to understand the particle size and distribution of soil particles; in the pharmaceutical industry, it is especially important to understand the particle size, content and distribution of each component of the drug; in the natural environment, people often use various methods to measure the content and distribution of sediment in rivers. All these show that in the production and life of modern society, particle measurement has been widely used and plays a very important role.

[0003] Natural rivers carry sediment, and the sediment carried in the water flow is beneficial or harmful to humans. On the one hand, sediment deposition can form a wetland ecosystem, and on the other hand, sediment deposition can block the river channel, causing the riverbed to rise, causing serious harm to human production and life. To solve the harm of sediment deposition, it is necessary to fully understand the characteristics of sediment carried in the water, and particle size is the most basic and important characteristic of particles. It is the most important parameter that determines the movement and sedimentation of sediment with the water flow. Therefore, the measurement of sediment particle size is a very important research direction. Sediment particles in rivers are mainly divided into two categories, namely, suspended sediment particles that move with the water flow and deposited bed sand. The present invention mainly studies the moving sediment particles that are more difficult to measure.

[0004] Traditional particle measurement methods have disadvantages such as complicated measurement steps, low measurement accuracy, high measurement cost and unstable measurement results. Summary of the invention

[0005] The present invention proposes a method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm.

[0006] The technical solution to realize the present invention is: a method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm, the specific steps are:

[0007] Step 1, collecting an image of a plane illuminated by a line laser underwater;

[0008] Step 2: Process the image to remove out-of-focus particles in the water and obtain the focused sediment particle distribution information.

[0009] Preferably, an underwater image acquisition device is used to acquire underwater patterns, wherein the underwater image acquisition device comprises an image acquisition module placed in a sealed cylinder, and at least one bottom surface of the sealed cylinder is made of a transparent material.

[0010] Preferably, the laser is incident parallel to the bottom surface of the transparent material sealed cylinder, and the incident laser forms a plane parallel to the bottom surface of the sealed cylinder in water. The image acquisition module adjusts the focusing plane to the plane irradiated by the laser, and acquires the image of the plane through the bottom surface of the transparent material sealed cylinder.

[0011] Preferably, the laser is a line laser.

[0012] Preferably, processing the image specifically includes image preprocessing, out-of-focus particle removal, threshold segmentation and morphological processing.

[0013] Preferably, the specific method of image preprocessing is:

[0014] Convert the original image into a grayscale image through linear grayscale transformation;

[0015] Bilateral filtering is used to remove noise from grayscale images.

[0016] Preferably, the specific method for removing out-of-focus particles is:

[0017] Convert the preprocessed image grayscale value to 0-1;

[0018] Convolve the image with the horizontal Gaussian kernel and the vertical Gaussian kernel respectively to obtain the horizontal and vertical gradient values;

[0019] The total gradient value is obtained by adding the horizontal and vertical gradient values. The gradient value can be used to describe whether the point is focused. After many experiments, a suitable threshold is selected. The point with a gradient value greater than the threshold is the focused point.

[0020] Preferably, the horizontal Gaussian kernel K1 and the vertical Gaussian kernel K2 are respectively as follows:

[0021]

[0022]

[0023] In the formula, n = 2m + 1, m is The nearest integer, σ and ε are set values, is the normalized kernel, g(x) and g(y) are the horizontal and vertical Gaussian kernel functions, respectively.

[0024] Preferably, the Gaussian kernel functions in the horizontal and vertical directions are respectively:

[0025]

[0026]

[0027] Where σ is the variance, x and y are the horizontal and vertical positions respectively.

[0028] Preferably, the specific method of threshold segmentation and morphological processing is:

[0029] The maximum inter-class variance method is used to perform threshold segmentation on the image after removing the out-of-focus particles, and an image is obtained in which the sediment particles and the background are separated.

[0030] The morphological closing and opening operations are performed on the image in sequence to count the distribution of sediment particles.

[0031] Compared with the prior art, the present invention has the following significant advantages: the present invention can accurately and quickly remove out-of-focus particles by combining line laser chip selection with a focusing algorithm; the present invention has high measurement accuracy, simple operation, and can be monitored in real time.

[0032] The present invention is described in further detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the device of the present invention.

[0034] Figure 2 This is a flowchart of the image processing of the present invention. DETAILED DESCRIPTION

[0035] A method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm, the specific method is as follows:

[0036] Step 1, using an underwater image acquisition device to capture underwater patterns, the underwater image acquisition device includes an image acquisition module and an image processing module placed in a sealed cylinder, at least one bottom surface of the sealed cylinder is made of transparent material, the underwater image acquisition device is placed underwater, the laser is incident parallel to the bottom surface of the sealed cylinder made of transparent material, the incident laser forms a plane parallel to the bottom surface of the sealed cylinder in water, the image acquisition module adjusts the focusing plane to the plane irradiated by the laser, and then captures the image of the plane through the bottom surface of the sealed cylinder made of transparent material.

[0037] In this embodiment, Figure 1As shown, E is the shell of the underwater image acquisition device, which is mainly used for sealing to prevent water from infiltrating. E is a hollowed-out metal cylinder, and the AB surface is transparent glass, which ensures good waterproofness while not affecting image acquisition. D represents a line laser, which is incident parallel to AB below AB, and the incident line laser will form a plane parallel to AB in the water. Figure 1 C in the middle is the image acquisition module and the image processing module. The image acquisition module is mainly a camera for collecting images, and the image processing module is a processor for processing images. The focusing plane of the image acquisition module is adjusted to the plane irradiated by the line laser, and then the image of the plane is collected. The line laser D is injected according to the position in the figure, and irradiates the water flow to form a plane. The light intensity on this plane is significantly stronger than other horizontal planes, so only the sediment particle information on this plane is retained.

[0038] In this embodiment, the camera selected is a SONY 1.3 million high-definition camera, and the CCD size is 1 / 3 `` The effective pixel is 1280×960, the pixel size is 3.4μm×3.4μm, and the exposure time can be as low as 0.01ms. The required sediment particle image can be obtained through the SONY130 camera.

[0039] Step 2: Use the image processing module to process the image to obtain the focused sediment particle distribution information.

[0040] In this embodiment, the image processing module uses a Tiny6410 development board, which is equipped with an S3C6410 processor. The development board has a very powerful image processing module, and a powerful hardware accelerator is integrated in the S3C6410 processor.

[0041] In this embodiment, the image is processed by an image processing module, which specifically includes image preprocessing, out-of-focus particle removal, threshold segmentation and morphological processing, which are respectively as follows:

[0042] Image preprocessing:

[0043] The image captured by the image acquisition module is an original color image, which is converted into a grayscale image through a linear grayscale transformation method.

[0044] Grayscale images contain a lot of useless noise interference. Bilateral filtering is used to remove noise. The formula for bilateral filtering is:

[0045]

[0046] In the formula, I p Represents the gray value of any pixel p in the gray image. After filtering, the gray value of this point is I′ p , I qrepresents the gray value of the neighborhood center point q, σ S is the size of the neighborhood that affects the central pixel, σ τ Indicates the influence of the difference between the central pixel and the surrounding pixels on the weighted value. represents the weighted function of the central pixel area, A weighting function that represents the difference between the central pixel and the surrounding pixels.

[0047] After bilateral filtering, the edge information of the sediment particles in the image is almost not lost, and most of the noise is well filtered out.

[0048] Out-of-focus particle removal:

[0049] The image after preprocessing still contains out-of-focus sediment particle information. In this embodiment, an algorithm is designed to remove these particles. The line laser itself can remove some out-of-focus particles, but some out-of-focus particle information is still preserved.

[0050] The specific method of processing images using the out-of-focus particle removal algorithm is as follows:

[0051] First, the grayscale value of the preprocessed image is converted to 0-1, and then the image is convolved with the horizontal Gaussian kernel and the vertical Gaussian kernel respectively to obtain the operation results, which represent the horizontal and vertical gradient values ​​respectively. In order to simplify the amount of calculation, the horizontal and vertical gradient values ​​are directly added, and the result is the total gradient value of the point. The gradient value can be used to describe whether the point is focused. After many experiments, a suitable threshold is selected. The point with a gradient value greater than the threshold is the focused point.

[0052] Gaussian kernel function in horizontal and vertical directions:

[0053]

[0054]

[0055] σ is the variance value, x and y represent the horizontal and vertical directions of the image respectively.

[0056] The definitions of the horizontal Gaussian kernel K1 and the vertical Gaussian kernel K2 are as follows:

[0057]

[0058]

[0059] K1 and K2 are matrices of order n×n (i.e., x and y are integer increments ranging from 0 to n), and n=2m+1 (m is The nearest integer), the values ​​of σ and ε in the formula are set by the user, is the normalization kernel, defined as follows:

[0060]

[0061] Threshold segmentation and morphological processing:

[0062] The maximum inter-class variance method was used to perform threshold segmentation on the image with out-of-focus particles removed, and an image with sediment particles and background separated was obtained. There were burrs and holes on the edges of particles in the binary image obtained after threshold segmentation, which could be solved by first performing a morphological closing operation on the image and then an opening operation, and finally statistically analyzing the distribution of sediment particles and other information.

Claims

1. A method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm, characterized in that: The specific steps are: Step 1, collecting an image of a plane illuminated by a line laser underwater; Step 2: Process the image to remove out-of-focus particles in the water and obtain the distribution information of focused sediment particles. The specific method for removing out-of-focus particles is as follows: Convert the preprocessed image grayscale value to 0-1; Convolve the image with the horizontal Gaussian kernel and the vertical Gaussian kernel respectively to obtain the horizontal and vertical gradient values; The horizontal Gaussian kernel K1 and the vertical Gaussian kernel K2 are as follows: In the formula, n = 2m + 1, m is The nearest integer, σ and ε are set values, is the normalized kernel, g ( x ) , g ( y ) They are Gaussian kernel functions in horizontal and vertical directions respectively; The Gaussian kernel functions in the horizontal and vertical directions are: Where σ is the variance, x and y are the horizontal and vertical positions respectively.

2. The method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm according to claim 1, characterized in that: Underwater patterns are collected using an underwater image collection device, wherein the underwater image collection device comprises an image collection module placed in a sealed cylinder, and at least one bottom surface of the sealed cylinder is made of a transparent material.

3. The method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm according to claim 2, characterized in that: The line laser is incident parallel to the bottom surface of the transparent material sealed cylinder. The incident line laser forms a plane parallel to the bottom surface of the sealed cylinder in water. The image acquisition module adjusts the focusing plane to the plane irradiated by the line laser and acquires the image of the plane through the bottom surface of the transparent material sealed cylinder.

4. The method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm according to claim 1, characterized in that: The image processing specifically includes image preprocessing, out-of-focus particle removal, threshold segmentation and morphological processing.

5. The method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm according to claim 4, characterized in that: The specific method of image preprocessing is: Convert the original image into a grayscale image through linear grayscale transformation; Bilateral filtering is used to remove noise from grayscale images.

6. The method for removing out-of-focus particles in water based on line laser chip selection and focusing algorithm according to claim 4, characterized in that: The specific methods of threshold segmentation and morphological processing are: The maximum inter-class variance method is used to perform threshold segmentation on the image after removing the out-of-focus particles, and an image is obtained in which the sediment particles and the background are separated. Perform morphological closing and opening operations on the image in turn to count the distribution of sediment particles; The total gradient value is obtained by adding the horizontal and vertical gradient values. The gradient value is used to describe whether the sediment particles are focused. After many experiments, a suitable threshold is selected. The sediment particles with a gradient value greater than the threshold are focused sediment particles.

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