Brightness controllable pseudo-color enhancement method based on multi-spectrum space fusion

Abstract

This invention discloses a brightness-controllable pseudo-color enhancement method based on multi-spectral color space fusion. The implementation steps are: quantizing a 14-bit infrared image into a 16-bit image and enhancing it using the algorithm constructed in this invention; constructing a high grayscale infrared image HIS pixel self-transformation mapping function and a brightness-adjustable RGB mapping function to map the enhanced 16-bit grayscale image to a 24-bit RGB color image. This invention solves the problem that 14-bit infrared images cannot be properly displayed on conventional 8-bit displays, and by mapping the 16-bit image to a 24-bit RGB image through the mapping function, it improves the visual quality of infrared images. It is highly practical and has high promotional value, and can be widely applied to image processing algorithms for vehicle-mounted infrared images, SAR images, and similar images, providing reliable application support for related fields.

Description

Technical Field

[0001] This invention belongs to the field of image processing technology, specifically relating to a brightness-controllable pseudo-color enhancement method based on multi-color space fusion. Background Technology

[0002] Environmental perception systems, as a crucial component of autonomous driving platforms, are the foundation and prerequisite for functions such as path planning and decision-making control. However, ordinary vehicle-mounted cameras are easily affected by factors such as darkness, lighting conditions, and weather, while infrared cameras can largely mitigate these issues. Currently, high-performance vehicle-mounted infrared imaging systems generally employ 14-bit digital-to-analog converters to sample and quantize the imaging system's output signal. However, conventional display devices only have an 8-bit data width. Therefore, when displaying 14-bit infrared images, the imaging system needs to perform image enhancement processing on the raw infrared image data.

[0003] Image processing technology is an important means of enhancing infrared images. Processed infrared images not only reduce the dangers of nighttime driving but also improve driver visibility in adverse weather conditions. Traditional methods for processing infrared images require manual parameter adjustment and cannot achieve adaptive enhancement, causing significant inconvenience to drivers and potentially leading to traffic accidents. The infrared ship image enhancement algorithm based on intuitionistic fuzzy sets and CLAHE proposed by Li Haijun et al. can improve image details, but it is limited to 8-bit infrared images and has weak adaptive capabilities. The infrared image pseudo-color enhancement algorithm based on multi-threshold segmentation proposed by Dai Shaosheng et al. can effectively highlight edge details and sharpen target outlines, but its performance is poor when processing low-contrast, low-light infrared images, exhibiting significant limitations. Therefore, existing technologies suffer from problems such as inability to display properly on conventional displays, large grayscale dynamic range, and low contrast, making it difficult to meet the needs of automotive infrared image display. Summary of the Invention

[0004] This invention proposes a brightness-controllable pseudo-color enhancement method based on multi-color space fusion to solve the problems of in-vehicle infrared images not being able to be displayed normally on conventional displays, having a large grayscale dynamic range, and low contrast.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A brightness-controllable pseudo-color enhancement method based on multi-chromatic space fusion includes the following steps:

[0007] Step 1: Process the original 14-bit high grayscale low contrast infrared image to obtain an enhanced 16-bit infrared image with higher contrast.

[0008] Step 2: Convert the grayscale of the 16-bit image output in Step 1 to 12-bit 4096 levels;

[0009] Step 3: Implement pseudo-color processing of the 12-bit infrared image using a high grayscale image pseudo-color enhancement algorithm. The high grayscale image pseudo-color enhancement algorithm specifically includes the following steps:

[0010] 3.1: Introducing a dynamic update factor for the chromatographic function enables dynamic adjustment of the chromatographic image and fixation of the target at the position of interest, as shown in equation (1) below:

[0011]

[0012] Among them, g δ (x,y) represents the dynamic update factor of the chromatographic function, and g(x,y) represents the gray value of the input image at point (x,y). γ represents the average grayscale value of the 12-bit infrared image, and max and min represent the maximum and minimum grayscale values ​​of the input image, respectively. The principle for chromatographic shift is as follows: if 1 < δ < 3, the chromatogram is shifted to the right; if 0 < δ < 1, the chromatogram is shifted to the left; if δ = 1, no chromatographic shift compensation processing is performed.

[0013] 3.2: Construct the HIS pixel self-transformation mapping function of the high-bit original infrared image, as shown in equation (2) below:

[0014]

[0015] in, H(x,y), I(x,y), and S(x,y) represent the three components of the HIS color space.

[0016] 3.3: By combining the HIS pixel self-transformation mapping function with the RGB mapping function, a brightness-adjustable pseudo-color mapping function for the high-bit original infrared image is constructed, as shown in equation (3) below:

[0017]

[0018] Where α = 1.2 represents the camera exposure gain, β = -10 represents the camera compensation offset, ω1 = 0.9 and ω2 = 0.1 represent the chromatographic allocation fusion weights, and R(x,y), G(x,y), and B(x,y) represent the red, green, and blue channels of the infrared pseudo-color image, respectively.

[0019] Furthermore, step one specifically includes the following steps:

[0020] 1.1: Input a 14-bit raw high grayscale low contrast infrared image;

[0021] 1.2: Quantize the input 14-bit raw high grayscale low contrast infrared image to generate a 16-bit high grayscale low contrast image.

[0022] 1.3: Enhancement processing is performed on the quantized 16-bit image;

[0023] 1.4: Adaptive enhancement processing is performed on the quantized 16-bit image;

[0024] 1.5: The two images designed in 1.3 and 1.4 are weighted and fused to obtain a 16-bit infrared enhanced image with higher contrast.

[0025] Furthermore, in step 1.3, the adaptive enhancement processing adopts the following equation (4):

[0026]

[0027] Where N1 and N2 represent 16-bit and 64-bit normalization functions, G represents the Gaussian function, f(x,y) and l(x,y) represent the gray values ​​of the input and output images at the (x,y) points, respectively, max and min represent the maximum and minimum gray values ​​of the input image, respectively, and D = 16 represents the quantized 16-bit image, sigma = 100.

[0028] Furthermore, in step 1.4, the fusion process adopts the following equation (5):

[0029] h(x,y)=ω×g(x,y)+(1-ω)×l(x,y) (5)

[0030] Where h(x,y) represents the gray value at point (x,y) after adaptive enhancement of the high grayscale image, and ω represents the weight coefficient. The enhancement effects of the two image enhancement methods each account for 50% of the weight, so ω = 0.5 is set.

[0031] Furthermore, in step 1.5, the enhancement process adopts the following equation (6):

[0032]

[0033] Where f(x,y) and g(x,y) represent the gray values ​​of the input and output images at the point (x,y), respectively, and max and min represent the maximum and minimum gray values ​​of the input image, respectively. D = 16 indicates that the bit depth of the output image is 16.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] (1) In view of the problem that the difference between the background and target gray values ​​of infrared images is small, the present invention reconstructs a high-bit SIN mapping function, which can process the background and target information of infrared images using different mapping curves, and perform adaptive enhancement processing for infrared images with different characteristics. It can effectively improve the visual effect of the image, enhance the interpretation and recognition effect of the image without affecting the image quality, and provide a better visual experience while ensuring the image quality.

[0036] (2) This invention combines the HIS pixel self-transformation model in the traditional HIS model with the pseudo-color model constructed in this invention, which can better achieve adaptive enhancement of wide dynamic range, low contrast, and high grayscale infrared images, and can achieve normal display in conventional displays.

[0037] (3) This invention improves upon the problem that existing pseudo-color algorithms can only process low-bit (256 levels) images and cannot adaptively enhance them. It constructs an adaptive mapping function suitable for high-bit (more than 8 bits) grayscale images, which can realize adaptive pseudo-color enhancement of wide dynamic range, low contrast, and high grayscale infrared images. Attached Figure Description

[0038] Figure 1 This is a block diagram illustrating the implementation of a brightness-controllable pseudo-color enhancement method based on multi-chromatographic space fusion according to the present invention.

[0039] Figure 2 The 14-bit original high grayscale low contrast infrared image is input to the method of this invention;

[0040] Figure 3 This is a high grayscale adaptive enhancement image created by the method of the present invention;

[0041] Figure 4 This is a chromatogram of the high grayscale pseudocolor enhancement algorithm of the present invention;

[0042] Figure 5 This is a pseudo-color enhancement image of a high grayscale image obtained by the method of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying embodiments. Obviously, the described embodiments are only some embodiments of this invention and are used only to illustrate the invention, but are not intended to limit the scope of the invention.

[0044] See Figure 1The basic idea of ​​the present invention is to first apply Gaussian blur, logarithmic domain transformation, SIN enhancement function and other methods to adaptively enhance the high-order grayscale image of the input 14-bit infrared image; then, the grayscale of the output 16-bit image is quantized to 12-bit 4096 level; finally, the pseudo-color enhancement algorithm of high grayscale image is used to realize the pseudo-color processing of the 12-bit infrared image.

[0045] Example:

[0046] Based on the above basic idea, this invention provides a method for brightness-controllable pseudo-color enhancement based on multi-chromatographic space fusion, comprising the following steps:

[0047] Step 1: A high grayscale adaptive enhancement algorithm suitable for over 8-bit images is used to process the original 14-bit high grayscale low-contrast infrared image to obtain a 16-bit infrared image with higher contrast after enhancement.

[0048] This step primarily addresses the issue that 14-bit infrared images cannot be displayed correctly on conventional 8-bit displays, and provides a high-contrast grayscale image for the subsequent pseudo-color enhancement algorithm. The specific steps are as follows:

[0049] 1.1: Input a 14-bit raw high grayscale low contrast infrared image.

[0050] like Figure 2 As shown, the original 14-bit high grayscale low contrast infrared image is only visible as black to the naked eye, with extremely low contrast, and cannot distinguish any effective information in the image.

[0051] 1.2: Quantize the input 14-bit original high grayscale low contrast infrared image to generate a 16-bit high grayscale low contrast image.

[0052] 1.3: To address the issue of small grayscale differences between the background and target in infrared images studied in this patent, and the characteristic that the sky portion of infrared images occupies a large proportion and is located at the top, an enhancement method based on the SIN function is designed to enhance the quantized 16-bit image. The equation is shown below.

[0053]

[0054] Where f(x,y) and g(x,y) represent the gray values ​​of the input and output images at the point (x,y), respectively, max and min represent the maximum and minimum gray values ​​of the input image, respectively, and D=16 indicates that the bit depth of the output image is 16.

[0055] 1.4: Adaptive enhancement processing is performed on the quantized 16-bit image using methods such as Gaussian blur and logarithmic domain transformation. The equation below illustrates this.

[0056]

[0057] Where N1 and N2 represent 16-bit and 64-bit normalization functions, G represents the Gaussian function, f(x,y) and l(x,y) represent the gray values ​​of the input and output images at the (x,y) points, respectively, max and min represent the maximum and minimum gray values ​​of the input image, D=16 represents the quantized 16-bit image, and sigma=100.

[0058] 1.5: Finally, the two image enhancement methods designed in 1.3 and 1.4 are fused to obtain a 16-bit infrared image with higher contrast after enhancement, as shown in the equation below.

[0059] h(x,y)=ω×g(x,y)+(1-ω)×l(x,y) (3)

[0060] Where h(x,y) represents the gray value at point (x,y) after adaptive enhancement of the high grayscale image. ω represents the weighting coefficient, with each of the two image enhancement methods accounting for 50% of the enhancement effect, therefore ω = 0.5 is set.

[0061] like Figure 3 As shown, the image enhanced by adaptive mapping clearly shows the overall outline, and the contrast is significantly improved compared to the original 14-bit high grayscale infrared image. However, the enhanced image still suffers from overall brightness, making it difficult to clearly distinguish target and background information, and exhibiting problems such as blurred details and difficulty in obtaining effective information.

[0062] Step 2: Convert the grayscale of the 16-bit image output in Step 1 to 12-bit 4096 levels. The calculation formula is as follows:

[0063]

[0064] Among them, I 16 I represents a 16-bit image grayscale matrix. 12 This represents the quantized 12-bit image grayscale matrix.

[0065] Step 3: Use a high grayscale image pseudo-color enhancement algorithm to perform pseudo-color processing on the 12-bit infrared image, achieving multi-color fusion and brightness-controllable high grayscale pseudo-color enhancement:

[0066] This patent constructs a dynamic update factor for the chromatogram function of a pseudo-color model. Addressing the brightness imbalance problem in high grayscale infrared images, it dynamically adjusts the chromatogram and locates the target at the position of interest. To address the insufficient brightness issue in existing pseudo-color model algorithms, the patent combines the constructed pseudo-color model with the HIS pixel self-transformation model. The steps of the high grayscale image pseudo-color enhancement algorithm are as follows:

[0067] 3.1: To achieve dynamic adjustment of chromatographic images and target the position of interest, a dynamic update factor for the chromatographic function is introduced, as shown in the following equation.

[0068]

[0069] Among them, g δ (x,y) represents the dynamic update factor of the chromatographic function, and g(x,y) represents the gray value of the input image at point (x,y). γ represents the average grayscale value of the 12-bit infrared image, and max and min represent the maximum and minimum grayscale values ​​of the input image, respectively. The principle for chromatographic shifting is as follows: if 1 < δ < 3, the chromatogram is shifted to the right; if 0 < δ < 1, the chromatogram is shifted to the left; if δ = 1, no chromatographic shift compensation processing is performed.

[0070] 3.2: Construct the HIS pixel self-transformation mapping function for the high-order original infrared image. As shown in the equation below.

[0071]

[0072] in, H(x,y), I(x,y), and S(x,y) represent the three components of the HIS color space.

[0073] 3.3: By combining the HIS pixel self-transformation mapping function with the RGB mapping function constructed in this patent, a brightness-adjustable pseudo-color mapping function for the high-bit original infrared image is constructed. This is shown in the equation below.

[0074]

[0075] Where α = 1.2 represents the camera exposure gain. β = -10 represents the camera compensation offset. ω1 = 0.9, ω2 = 0.1 represent the chromatographic allocation fusion weights. R(x,y), G(x,y), and B(x,y) represent the red, green, and blue channels of the infrared pseudo-color image, respectively.

[0076] like Figure 4 As shown, the trend chart of the 4096-level high grayscale pseudo-color function can be seen, where the input grayscale level is filled with grayscale values ​​from 0 to 4095.

[0077] like Figure 5 As shown, the pseudo-color image after pseudo-color enhancement can be clearly distinguished by the naked eye, with high image contrast, clear details, and significantly improved visual effect.

[0078] The above description is a specific illustration of the present invention, and not a limitation thereof. Those skilled in the art can make various equivalent technical solutions without departing from the scope of the present invention; therefore, all equivalent technical solutions should fall within the patent protection scope of the present invention.

Claims

1. A method for enhancing pseudo-color with controllable brightness based on multi-chromatographic space fusion, characterized in that, Includes the following steps: Step 1: Process the original 14-bit high grayscale low contrast infrared image to obtain an enhanced 16-bit infrared image with higher contrast. Step 2: Convert the grayscale of the 16-bit image output in Step 1 to 12-bit 4096 levels; Step 3: Implement pseudo-color processing of the 12-bit infrared image using a high grayscale image pseudo-color enhancement algorithm. The high grayscale image pseudo-color enhancement algorithm specifically includes the following steps: 3.1: Introducing a dynamic update factor for the chromatographic function enables dynamic adjustment of the chromatographic image and fixation of the target at the position of interest, as shown in equation (1) below: Among them, g δ (x,y) represents the dynamic update factor of the chromatographic function, and g(x,y) represents the gray value of the input image at point (x,y). γ represents the average grayscale value of the 12-bit infrared image, and max and min represent the maximum and minimum grayscale values ​​of the input image, respectively. The principle for chromatographic shift is as follows: if 1 < δ < 3, the chromatogram is shifted to the right; if 0 < δ < 1, the chromatogram is shifted to the left; if δ = 1, no chromatographic shift compensation processing is performed. 3.2: Construct the HIS pixel self-transformation mapping function of the high-bit original infrared image, as shown in equation (2) below: in, H(x,y), I(x,y), and S(x,y) represent the three components of the HIS color space; 3.3: By combining the HIS pixel self-transformation mapping function with the RGB mapping function, a brightness-adjustable pseudo-color mapping function for the high-bit original infrared image is constructed, as shown in equation (3) below: Where α = 1.2 represents the camera exposure gain, β = -10 represents the camera compensation offset, ω1 = 0.9 and ω2 = 0.1 represent the chromatographic allocation fusion weights, and R(x,y), G(x,y) and B(x,y) represent the red, green and blue channels of the infrared pseudo-color image, respectively. Step one specifically includes the following steps: 1.1: Input a 14-bit raw high grayscale low contrast infrared image; 1.2: Quantize the input 14-bit raw high grayscale low contrast infrared image to generate a 16-bit high grayscale low contrast image. 1.3: Enhancement processing is performed on the quantized 16-bit image; 1.4: Adaptive enhancement processing is performed on the quantized 16-bit image; 1.5: The two images designed in 1.3 and 1.4 are weighted and fused to obtain a 16-bit infrared enhanced image with higher contrast.

2. The brightness-controllable pseudo-color enhancement method based on multi-chromatographic space fusion according to claim 1, characterized in that, In step 1.3, the adaptive enhancement processing adopts the following equation (4): Where N1 and N2 represent 16-bit and 64-bit normalization functions, G represents the Gaussian function, f(x,y) and l(x,y) represent the gray values ​​of the input and output images at the (x,y) points, respectively, max and min represent the maximum and minimum gray values ​​of the input image, respectively, and D = 16 represents the quantized 16-bit image, sigma = 100.

3. The brightness-controllable pseudo-color enhancement method based on multi-chromatographic space fusion according to claim 2, characterized in that, In step 1.4, the fusion process is performed using the following equation (5): h(x,y)=ω×g(x,y)+(1-ω)×l(x,y) (5) Where h(x,y) represents the gray value at point (x,y) after adaptive enhancement of the high grayscale image, and ω represents the weight coefficient. The enhancement effects of the two image enhancement methods each account for 50% of the weight, so ω = 0.5 is set.

4. The brightness-controllable pseudo-color enhancement method based on multi-chromatographic space fusion according to claim 3, characterized in that, In step 1.5, the enhancement process is performed using the following equation (6): Where f(x,y) and g(x,y) represent the gray values ​​of the input and output images at the point (x,y), respectively, and max and min represent the maximum and minimum gray values ​​of the input image, respectively. D = 16 indicates that the bit depth of the output image is 16.

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