Infrared image display method

Abstract

The invention relates to the field of image display, in particular to an infrared image display method, which comprises the following steps of: S1, performing respe operation on an input temperature array; s2, converting the temperature array into a temperature matrix; s3, performing smoothing operation by using a Laplacian algorithm; s4, normalizing the temperature matrix into a gray value; s5, colors are distributed according to the color map of each pixel; s6, processing the low-resolution image by using a super-resolution algorithm; according to the invention, the readability of the image can be improved, and the visualization of information is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of image display, and particularly to an infrared image display method. Background Art

[0002] In the process of medical diagnosis, infrared thermal imaging technology is widely used in fields such as body temperature monitoring and disease detection. Traditional infrared image color bars (pseudo-color bars) often use hue (H value) and saturation (S value) to display temperature changes. However, the visual effect of existing color bars in high-temperature regions is not obvious, resulting in blurred display in high-temperature regions, making it difficult for doctors to quickly and accurately detect temperature abnormal regions, thus affecting the diagnosis efficiency and accuracy.

[0003] Especially in some high-temperature areas, traditional color bars are difficult to provide sufficient contrast, resulting in low recognition of these abnormal regions in the image and difficult to quickly locate. Summary of the Invention

[0004] The present invention aims to provide an improved infrared image display method to solve the problems that the infrared image display in the prior art does not conform to the human eye temperature perception and is inconvenient to view. Through the improved display method, the readability of the image can be improved, the visualization of information can be enhanced, and thus more effective technical support can be provided for the applications in related fields.

[0005] The object of the present invention is achieved by the following measures: An infrared image display method, comprising the following steps:

[0006] S1. Perform a reshape operation on the input temperature array;

[0007] S2. Convert the temperature array into a temperature matrix;

[0008] S3. Perform a smoothing operation using the Laplace algorithm;

[0009] S4. Normalize the temperature matrix to grayscale values;

[0010] S5. Assign colors according to the colormap of each pixel;

[0011] S6. Process the low-resolution image using a super-resolution algorithm.

[0012] Preferably, receive the input temperature array, which is a matrix of 1 row × 384 columns × 288 columns. Perform a reshape operation on the input temperature array to convert it into a temperature matrix that meets the requirements of image display, facilitating subsequent processing and display.

[0013] Preferably, for the image smoothing process in step S3, specifically as follows:

[0014] The Laplace algorithm is used to smooth the temperature matrix. The Laplace algorithm is a commonly used image enhancement technique. By performing a second-order differential operation on the image, it can highlight the edge and detail information in the image, suppress noise, and improve the smoothness of the temperature matrix. The specific calculation method is as follows:

[0015]

[0016] Among them, T(i,j) is the temperature value at position (i,j), α is the smoothing parameter, N is the total number of elements in the data matrix, and T smoothed (i,j) is the smoothed temperature. The smoothing operation reduces the influence of extreme values and outliers by adjusting the original temperature matrix, providing clearer basic data for subsequent operations such as normalization and color mapping.

[0017] Preferably, the normalization of the temperature matrix in step S4 is as follows:

[0018] Use ROI (Region of Interest) to determine the maximum and minimum values for normalization. Specifically, the maximum temperature value of the entire pattern is used as the maximum value for normalization, and the average value of 50 pixels in the upper left and upper right regions is used as the minimum value for normalization. The normalization method based on ROI can adapt to the temperature ranges in different scenarios, ensuring that the normalized grayscale values can accurately reflect the relative differences in temperature. The temperature matrix is normalized according to the determined maximum and minimum values, and converted into values between 0 and 1 for subsequent grayscale value conversion and color mapping display. The specific calculation method is as follows:

[0019]

[0020] Among them, T(i,j) is the element value in the temperature matrix, T max is the maximum value of the entire image, and T min is the minimum value obtained by calculating the average value of 50 pixels in the upper left and upper right corners. 255 is the maximum grayscale value of the grayscale image.

[0021] Assume that 50 elements are extracted from the upper left and upper right corners respectively, and their temperature values are T left (i,j) and T right (i,j), then the minimum value T min is the average value of these two groups of pixels.

[0022]

[0023] Preferably, the specific calculation method of the super-resolution algorithm is as follows:

[0024]

[0025] Among them, I high(x, y) is the high-resolution pixel value of the super-resolution image, I low (x, y) is the pixel value of the low-resolution image, ω i,j (x, y) is the weight function, the weight calculated according to image interpolation, and (x + i, y + j) represents the coordinates of adjacent pixels in the low-resolution image.

[0026] According to the gray value of each pixel, the corresponding color is assigned to it according to the preset colormap (color mapping table). The colormap is a relationship that maps gray values to specific colors. Through a carefully designed colormap, different temperature ranges can be mapped to different color intervals, making the infrared image more intuitive and conforming to the temperature perception law of the human eye. High-temperature areas can be mapped to warm colors such as red, and low-temperature areas are mapped to cold colors such as blue, which helps professionals such as doctors quickly identify and analyze the temperature distribution.

[0027] Advantages of the present invention: The present invention can more effectively enhance the visibility of temperature anomaly areas. At the same time, the design of the pseudo-color bar in the infrared image display method can be applied to medical image processing software. By calculating the corresponding relationship between the temperature range and the brightness value, a pseudo-color image with high contrast is generated, which can improve the visibility of temperature anomaly areas in medical imaging images and enhance the accuracy and reliability of image diagnosis. Description of the Drawings

[0028] Figure 1 It is a flowchart of an infrared image display method. Detailed Embodiments

[0029] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] Embodiment 1: As Figure 1 shown, the embodiment of the present invention is used for an infrared image display method, and the specific operation steps are as follows:

[0031] Step S1, perform a reshape operation on the input temperature array;

[0032] Receive the input temperature array, which is a matrix of 1 row × 384 columns × 288 columns. Perform a reshape operation on the input temperature array to convert it into a temperature matrix that meets the requirements of image display, facilitating subsequent processing and display.

[0033] Step S2: Convert the temperature array into a temperature matrix;

[0034] In the field of image processing, an image is usually represented as a two-dimensional matrix, and temperature data also needs to be converted into a matrix form that matches the image size for subsequent processing (such as grayscale conversion, normalization, etc.). At this time, the temperature array can be regarded as the pixel data of an image of a certain size, and converting it into a temperature matrix is to reorganize the one-dimensional data into a two-dimensional matrix.

[0035] Step S3: Perform a smoothing operation using the Laplace algorithm;

[0036] Use the Laplace algorithm to smooth the temperature matrix. By performing a second-order differential operation on the image, it is possible to highlight the edge and detail information in the image, suppress noise, and improve the smoothness of the temperature matrix. The specific calculation method is as follows:

[0037]

[0038] where T(i,j) is the temperature value at position (i,j), α is the smoothing parameter, N is the total number of elements in the data matrix, and T smoothed (i,j) is the smoothed temperature. The smoothing operation reduces the influence of extreme values and outliers by adjusting the original temperature matrix, providing clearer basic data for subsequent operations such as normalization and color mapping.

[0039] Step S4: Normalize the temperature matrix to grayscale values;

[0040] Use ROI (Region of Interest) to determine the maximum and minimum values for normalization. Take the maximum temperature value of the entire pattern as the maximum value for normalization, and take the average value of 50 pixels in the upper left and upper right regions as the minimum value for normalization. The normalization method based on ROI can adapt to temperature ranges in different scenarios, ensuring that the normalized grayscale values can accurately reflect the relative differences in temperature. Normalize the temperature matrix according to the determined maximum and minimum values, and convert it into grayscale values between 0 and 1 for subsequent color mapping and display. The specific calculation method is as follows:

[0041]

[0042] where T(i,j) is the element value in the temperature matrix, T max is the maximum value of the entire image, and T min is the minimum value obtained by calculating the average value of 50 pixels in the upper left and upper right corners, and 255 is the maximum grayscale value of the grayscale image.

[0043] Suppose 50 elements are extracted from the upper left and upper right corners respectively, and their temperature values are T left (i,j) and T right(i,j), then the minimum value T min is the average value of these two groups of pixels;

[0044]

[0045] Step S5. Assign colors according to the colormap of each pixel;

[0046] According to the gray value of each pixel, assign corresponding colors to it based on a preset colormap (color mapping table). A colormap is a relationship that maps gray values to specific colors. By designing the colormap, different temperature ranges can be mapped to different color intervals, making the infrared image more intuitive and conforming to the temperature perception law of the human eye. High-temperature areas can be mapped to warm colors such as red, and low-temperature areas can be mapped to cold colors such as blue, which helps professionals such as doctors quickly identify and analyze the temperature distribution.

[0047] In this step, the HSV (hue, saturation, value) color model is adopted. The brightness (V value) of the color bar is adjusted to represent different temperature ranges. Among them, the higher the temperature area, the larger the corresponding brightness value, thereby enhancing the visual contrast between high and low temperature areas and improving the recognition accuracy of temperature anomaly areas. The hue (H value) of the pseudo-color bar has a certain mapping relationship with the temperature range, and the saturation (S value) remains within a relatively stable range. Only the change in brightness (V value) is used to reflect the temperature change. The brightness value of the pseudo-color bar gradually increases with the increase of temperature. The brightness value of the low-temperature area is low, and the brightness value of the high-temperature area increases significantly. The pseudo-color bar has strong brightness in the high-temperature area, ensuring a significant contrast between the colors of the high-temperature area and the low-temperature area, which is convenient for the human eye to distinguish and recognize.

[0048] The design of this pseudo-color bar can also be applied to medical image processing software. By calculating the corresponding relationship between the temperature range and the brightness value, a pseudo-color image with high contrast is generated.

[0049] Step S6. Process the low-resolution image using a super-resolution algorithm;

[0050] The specific calculation method is as follows:

[0051]

[0052] Among them, I high (x, y) is the high-resolution pixel value of the super-resolution image, I low (x, y) is the pixel value of the low-resolution image, ω i,j (x, y) is the weight function, the weight calculated according to image interpolation, and (x + i, y + j) represents the coordinates of adjacent pixels in the low-resolution image.

[0053] In summary, the present invention solves the problems that the traditional infrared image display method has unclear color differentiation in high-temperature regions and it is difficult to effectively distinguish temperature abnormal regions. The traditional infrared image display method usually uses relatively similar colors in high-temperature regions, resulting in a low contrast between the regions with higher temperatures and the background, making it difficult to accurately identify temperature abnormal regions in medical imaging. The present invention introduces the adjustment of the brightness change of HSV (hue, saturation, value) in the design of the pseudo-color bar. By adjusting the brightness, the brighter the region with a higher temperature, and the color gradually becomes more distinct and easily recognizable by the human eye, thus effectively enhancing the visualization effect of high-temperature regions. This innovative design greatly improves the accuracy of temperature differentiation, especially applicable to regions with large temperature changes in medical imaging, helping doctors quickly discover and locate abnormal parts.

[0054] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An infrared image display method, characterized in that: The following steps are involved: S1, reshape the input temperature array; S2, convert the temperature array into a temperature matrix; S3, use Laplace algorithm for smoothing operation; S4, normalizing the temperature matrix into grayscale values; S5, assigning colors according to the colormap of each pixel; S6. Use super-resolution algorithms to process low-resolution images.

2. The infrared image display method according to claim 1, characterized in that: The received input temperature array is a matrix of 1 row × 384 columns × 288 columns. A reshape operation is performed on the input temperature array to convert it into a temperature matrix that meets the image display requirements.

3. The infrared image display method according to claim 1, characterized in that: In step S3, the Laplace algorithm performs a smoothing operation, which is specifically as follows: Where T(i,j) is the temperature at position (i,j), α is the smoothing parameter, N is the total number of elements in the data matrix, and T smoothed (i, j) is the smoothed temperature. The smoothing operation reduces the impact of extreme values ​​and outliers by adjusting the original temperature matrix.

4. The infrared image display method according to claim 1, characterized in that: In step S4, the temperature matrix is ​​normalized to grayscale values, and the normalized maximum and minimum values ​​are determined according to the ROI region of interest. The maximum value of the entire image is taken as the maximum value, and 50 pixels are extracted from the upper left and upper right to take the average value as the minimum value, as follows: Where T(i,j) is the element value in the temperature matrix, T max is the highest value of the entire image, T min It is the lowest value obtained by calculating the average value of 50 pixels in the upper left and upper right corners, and 255 is the maximum grayscale value of a grayscale image; Suppose 50 elements are drawn from the upper left corner and the upper right corner, and their temperature values ​​are T left (i,j) and T right (i,j), then the lowest value T min is the average value of these two groups of pixels; 5. The infrared image display method according to claim 1, characterized in that: In step S6, a super-resolution algorithm is used to process the low-resolution image, specifically as follows: Among them, I high (x, y) is the high-resolution pixel value of the super-resolution image, I low (x,y) is the pixel value of the low-resolution image, ω i,j (x, y) is the weight function, which is the weight calculated by image interpolation, and (x+i, y+j) represents the coordinates of adjacent pixels in the low-resolution image.

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