A front-looking sonar image pseudo-color processing method

Abstract

This invention discloses a pseudo-color processing method for forward-looking sonar images. It linearly normalizes the statistical intensity range by using a dynamic threshold based on the current sonar echo intensity. A custom Jet color bar is generated based on the characteristics of the forward-looking sonar image, achieving pixel-level adaptive color mapping. This method significantly increases the visualization quality and target identification of sonar images, improving the operator's perception and judgment of underwater targets. It overcomes the detail loss problem caused by existing technologies using only fixed pseudo-color mapping, has broad application prospects, and requires only software implementation, possessing good compatibility and promotional value.

Description

Technical Field

[0001] This invention belongs to the field of image processing technology and relates to an image pseudo-color processing method. Background Technology

[0002] The development of underwater detection and operation technologies has become a key area of ​​focus both domestically and internationally. Forward-looking sonar, with its core advantage of outputting high-resolution echo images with a fan-shaped field of view underwater, is widely used in underwater target detection scenarios. Forward-looking sonar equipment detects the ocean through sound waves, generating data sequences by collecting multiple sound wave intensities. However, raw sonar images often suffer from low resolution, severe speckle noise, and unclear features. When presented directly as grayscale images, the information expression is limited, and the human eye's ability to distinguish grayscale levels is poor, making it difficult to effectively identify details such as underwater targets, obstacles, or topographical features.

[0003] To enhance the visualization and information recognition of sonar images, pseudo-color processing techniques are widely used. This technique maps the monotonous grayscale value of sonar echo intensity to a color space more sensitive to the human eye, using different colors to distinguish different echo intensities, thereby highlighting image details and depth. Among these, Jet color mapping has become a commonly used pseudo-color mapping scheme due to its high contrast and natural color transitions. The literature "AUV Target Recognition and Tracking Based on Forward-Looking Sonar Images" provides a detailed description of the standard Jet color mapping implementation, demonstrating that Jet color mapping has been widely used for pseudo-colorization of forward-looking sonar images. Experimental results prove that Jet color mapping can improve visual contrast and target recognition. The patent "An Image Quality Optimization Method for Forward-Looking Sonar" proposes a quality optimization process for forward-looking sonar images, involving the implementation details of image preprocessing, dynamic compression, and color mapping, and points out the advantages of Jet in contrast and detail rendering. Existing literature provides detailed descriptions of the specific implementation details of Jet color mapping, verifying the maturity of forward-looking sonar Jet color mapping technology. However, current literature only demonstrates the advantages of standard Jet color mapping in pseudo-color processing of forward-looking sonar images, and has not optimized Jet color mapping for the characteristics of forward-looking sonar detection images and water turbidity.

[0004] In summary, existing pseudo-color processing techniques for forward-looking sonar images lack the ability to adapt to the inherent statistical characteristics of sonar echo signals and changes in the external aquatic environment, which limits their effectiveness in complex and ever-changing marine environments. There is an urgent need for an intelligent processing method that can dynamically optimize color mapping strategies. Summary of the Invention

[0005] In view of this, the present invention provides a pseudo-color processing method for forward-looking sonar images, which generates a custom grayscale-color mapping and dynamically adjusts the pseudo-color mapping strategy according to the real-time echo intensity distribution of the forward-looking sonar, so as to significantly improve the clarity, contrast and information recognizability of sonar images in different scenes and environments.

[0006] The specific technical solution is as follows: A method for pseudo-color processing of forward-looking sonar images, comprising the following: A custom Jet color map adapted to forward-looking sonar images is generated based on the standard Jet color map. The interval optimization logic of the custom Jet color map is as follows: Based on the analysis of a large number of original grayscale images of forward-looking sonar, the echo data intensity of the forward-looking sonar detected target (such as mine) is located in the middle area of ​​the overall intensity range. In the interval of [0, 1 / 8] of the standard Jet color bar, the color gradient is changed to increase the proportion of black, improve the overall contrast, and make the background noise map to the black and dark blue areas to weaken the noise. The interval of [1 / 8, 3 / 8] is refined into two segments: blue to cyan and cyan to green. In the interval of [1 / 8, 5 / 8], green is increased and blue is reduced to expand the coverage range of cyan, green and yellow series corresponding to the target, so that the target color to be distinguished is mapped to green and yellow series to increase the brightness of the target color. In the interval of [5 / 8, 1], yellow is increased and red is reduced to weaken the visual proportion of strong interference areas. This is used to improve the distinction between the target and interference and noise and the target clarity after pseudo-color rendering. Furthermore, the specific implementation of the custom Jet color mapping is as follows: the number of color arrays is set to... The custom Jet color mapping is provided by A one-dimensional array consisting of color nodes Each color node corresponds to one Value, set x as the index of the color array, with a range of values. These are the quantization values ​​for the red, green, and blue channels. Furthermore, based on the aforementioned interval optimization logic, the value range of index x is divided into 6 sub-intervals, and a set of custom color arrays is generated by traversing x. When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When implementing the above color array in a computer language, Each channel value will be limited to [0,1]. Channel values ​​greater than 1 will be set to 1 by default, and channel values ​​less than 0 will be set to 0 by default.

[0007] Furthermore, the sonar image to pseudo-color image conversion specifically involves: acquiring sonar images, statistically analyzing the forward-looking sonar echo intensity in real time, and dynamically setting a color mapping threshold based on the statistical results; using the color mapping threshold as a dynamic range, normalizing the grayscale value of each pixel G(i,j) in the sonar image to... The interval is used to obtain the normalized pixel values. ; Normalized pixel values Assign the value to the color index x, and pass it in. Find the corresponding RGB color value; assign the obtained RGB color value to the pixel at the corresponding position in the output pseudocolor image.

[0008] Furthermore, the maximum grayscale value of the sonar image is taken. and minimum value A color mapping threshold is set, which is updated in real time based on the acquired grayscale image to adapt to changes in the environment.

[0009] Beneficial effects 1. Enhanced image contrast and detail: By dynamically setting thresholds based on real-time echo intensity statistics, it can adaptively and optimally stretch the echo intensity of the current scene, avoiding images that are too dark or too bright due to scene changes, increasing color contrast, and greatly improving the effective information content of the image.

[0010] 2. Output the corresponding RGB colors based on the custom Jet color bar to achieve pixel-level color allocation, ultimately generating a high-contrast, information-rich pseudo-color sonar image. This avoids contrast compression caused by globally uniform gain.

[0011] 3. The color mapping process has low computational cost, making it suitable for real-time processing and display of forward-looking sonar images.

[0012] 4. This method is compatible with existing forward-looking sonar hardware, is implemented only at the software level, and has good engineering portability.

[0013] 5. The color mapping function uses piecewise interpolation to ensure smooth color transitions. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the method.

[0015] Figure 2 This is a sonar image processed using the standard Jet color bar pseudo-color method.

[0016] Figure 3 This method provides a custom Jet color bar pseudo-color processing sonar image. Detailed Implementation

[0017] The specific steps of this method are as follows: 1. Generate a color array for a custom Jet color bar. The number of colors in the array is (Generally set to 343), it consists of a series of Value composition, where x is the color array index, and the value range is... Divide the range of values ​​for index x into 6 sub-ranges, and iterate through x to generate a set of custom one-dimensional arrays. When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as .

[0018] The standard Jet color bar is divided into five segments: [0, 1 / 8] is blue, [1 / 8, 3 / 8] is a gradient from blue to cyan, [3 / 8, 5 / 8] is a gradient from cyan to yellow, [5 / 8, 7 / 8] is a gradient from yellow to red, and [7 / 8, 1] is red. Based on the analysis of a large number of forward-looking sonar raw grayscale images, strong echo data usually manifests as interference (interference caused by the hull, other acoustic equipment, etc.) and terrain. The echo data intensity of the target or obstacle to be distinguished is located in the middle area of ​​the overall intensity range. The middle area of ​​the standard Jet color bar is cyan and accounts for a very small proportion. After pseudo-color mapping, the brightness and clarity of the target are not high.

[0019] Therefore, the custom color bar re-divided the intervals, changing the five segments of the standard Jet color bar to six segments: [0, 1 / 8] is a black to blue gradient, [1 / 8, 2 / 8] is a blue to cyan gradient, [2 / 8, 3 / 8] is a cyan to green gradient, [3 / 8, 5 / 8] is a green to yellow gradient, [5 / 8, 7 / 8] is a yellow to red gradient, and [7 / 8, 1] is red. In the [0, 1 / 8] interval, the custom color bar increases the proportion of black by changing the degree of color gradient. The color contrast of the custom color bar after pseudo-color rendering is higher than that of the standard color bar. In the [1 / 8, 5 / 8] interval, the proportion of green is increased and the proportion of blue is decreased. In the [3 / 8, 5 / 8] interval, the proportion of yellow is increased and the proportion of red is decreased. This makes the central area of ​​the custom color bar green and yellow, increasing the clarity and brightness of the target pseudo-color rendering.

[0020] 2. Real-time acquisition of raw echo data from forward-looking sonar, and statistical analysis of the color mapping threshold for sonar echo intensity: Acquire a frame of sonar image data, traverse all pixels of the frame, and calculate the maximum grayscale value. and minimum value ,Will and Set as the color mapping threshold; 3. For each pixel G(i,j) in the sonar image G, perform the following mapping logic: Normalization: First, obtain the number of colors in the custom Jet color bar. , will pixels by The dynamic threshold range is normalized to Within the range, its normalization function is:

[0021] The normalized grayscale image has the following pixel count: ; Color search: The intensity value Use the index to find its corresponding RGB color value.

[0022] Pixel assignment: Assign RGB values ​​to the output pseudo-color image. corresponding pixels .

[0023] After traversing all pixels, an adaptively enhanced pseudo-color forward-looking sonar image is generated, which is then presented to the user via a display terminal. Figure 3 This is a sonar image processed with a custom Jet color bar pseudo-color, for comparison. Figure 2As can be seen, the custom color bar is closer to black in low-intensity areas compared to the standard Jet color bar, increasing color contrast. The image has higher color contrast, and the rendered weak echo data is darker, making the target shown in the box stand out better against the darker background. After rendering with the custom color bar, the target outline is clearer, the brightness is stronger, and it is easier for the naked eye to distinguish it. Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make several modifications without departing from the principles of the present invention, and these modifications should also be considered to fall within the scope of protection of the present invention.

Claims

1. A method for processing pseudo-color in forward-looking sonar images, characterized in that: A custom Jet color map adapted to forward-looking sonar images is generated based on the standard Jet color map. The interval optimization logic of the custom Jet color map is as follows: Based on the analysis of a large number of original grayscale images of forward-looking sonar, the echo data intensity of the forward-looking sonar detected target (such as mine) is located in the middle area of ​​the overall intensity range. In the interval of [0, 1 / 8] of the standard Jet color bar, the color gradient is changed to increase the proportion of black, improve the overall contrast, and make the background noise map to the black and dark blue areas to weaken the noise. The interval of [1 / 8, 3 / 8] is refined into two segments: blue to cyan and cyan to green. In the interval of [1 / 8, 5 / 8], green is increased and blue is reduced to expand the coverage range of cyan, green and yellow series corresponding to the target, so that the target color to be distinguished is mapped to green and yellow series to increase the brightness of the target color. In the interval of [5 / 8, 1], yellow is increased and red is reduced to weaken the visual proportion of strong interference areas. This is used to improve the distinction between the target and interference and noise and the target clarity after pseudo-color rendering. The sonar image is converted into a pseudo-color image using the custom Jet color mapping relationship.

2. The method according to claim 1, characterized in that: The specific implementation of the custom Jet color mapping is as follows: The number of color arrays is set to... The custom Jet color mapping is expressed as follows: A one-dimensional array consisting of color nodes Each color node corresponds to one Value, set x as the index of the color array, with a range of values. Quantization values ​​for the red, green, and blue channels.

3. The method according to claim 2, characterized in that: Based on the interval optimization logic, the range of index x is divided into 6 sub-intervals, and a set of custom color arrays is generated by traversing x. When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as When x iterates through the values ​​in Within the interval, The value is represented as .

4. The method according to claim 2, characterized in that: The sonar image to pseudo-color image conversion specifically involves acquiring sonar images, statistically analyzing the forward-looking sonar echo intensity in real time, and dynamically setting a color mapping threshold based on the statistical results. Using the aforementioned color mapping threshold as the dynamic range, the grayscale value of each pixel G(i,j) in the sonar image is normalized to... The interval is used to obtain the normalized pixel values. ; Normalized pixel values Assign the value to the array index x, and substitute it with... Find the corresponding RGB color value; assign the obtained RGB color value to the pixel at the corresponding position in the output pseudocolor image.

5. The method according to any one of claims 1-4, characterized in that: Take the maximum gray value of the sonar image and minimum value A color mapping threshold is set, which is updated in real time based on the acquired grayscale image to adapt to changes in the environment.

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