An adaptive wide gray scale range infrared image highlight target suppression method

Abstract

The present application relates to a kind of self-adapting wide gray scale range infrared image highlight target suppression method, belong to infrared image processing technical field.The present application utilizes the histogram information of original image, whether according to the decision function adaptive judgment enters highlight suppression algorithm processing flow according to statistical result, histogram of image is gathered and compressed in the flow, while using nonlinear mapping method, to achieve the purpose of effectively suppressing highlight target, so that highlight target and image background are effectively and reasonably displayed in display device.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of infrared image processing, and particularly relates to a self-adaptive wide gray scale range infrared image highlight target suppression method. BACKGROUND

[0002] With the increasingly wide application of infrared imaging systems in military detection and civilian life, people's requirements for the image quality of infrared images and the adaptability of scenes are also increasingly high. The original data of infrared images are often 14-bit wide gray scale range data, and in the process of infrared image processing, it is usually necessary to convert them into 8-bit image data for display and transmission through dynamic range compression technology, and at the same time, the contrast and details are improved, so as to enhance the display effect.

[0003] Image enhancement, as an important part of image processing, plays an important role in improving image quality. With the continuous deepening of the research on image enhancement technology, new image enhancement methods have appeared. At present, image enhancement methods are mainly divided into the following categories. The method based on gray scale mapping is a common processing technology for adjusting the gray scale dynamic range of infrared images. The commonly used mapping curve types are as follows: linear transformation mapping, logarithmic transformation, gamma correction and Semi-S curve mapping. Due to the great limitations of fixed curve gray scale mapping, an enhancement algorithm based on adaptive selection of mapping curve according to different decision criteria is developed. This method has lower algorithm complexity and higher engineering feasibility, but the design requirements for the decision function are also higher.

[0004] However, in some special scenes, for example, when high-temperature and high-heat objects enter the scene, the traditional linear enhancement method cannot fully utilize the effective display range of the display device, and the background information will be overwhelmed, making the overall image tone too dark, so that the background information cannot be effectively enhanced. Invalid gray scale space occupies the effective display range. Due to the lack of full and effective use of limited display space, important image details cannot be effectively displayed. SUMMARY

[0005] (I) Technical problem to be solved

[0006] The technical problem to be solved by the present application is to design a scene-adaptive infrared image highlight suppression method, so that the highlight target and the image background can be effectively and reasonably displayed in the display device.

[0007] (II) Technical scheme

[0008] In order to solve the above technical problems, the present application provides a self-adaptive wide gray scale range infrared image highlight target suppression method, comprising the following steps:

[0009] Step one: obtaining original infrared image;

[0010] Step two: non-uniform correction and bad cell replacement processing on the original infrared image;

[0011] Step three: histogram statistics on the processed infrared image, and obtaining image adjacent upper limit gray value and adjacent lower limit gray value information according to the statistics result, judging whether to enter the adaptive highlight suppression algorithm processing flow through Schmidt trigger type threshold, if the opening condition is met, entering step four, if not, entering step seven;

[0012] Step four: histogram collection compression processing, and collecting the gray pixels to give up image display space for other gray regions;

[0013] Two threshold values are needed in the implementation process of the designed compressed histogram, one is the gray control threshold T judge , if the gray value statistics is less than the threshold, the next gray value statistics is collected to the current gray level, and the subsequent gray value statistics is advanced; if the accumulated statistics of the current gray level and the next gray level still does not exceed the accumulated threshold T sum , the next gray value statistics is collected to the current gray level, until the accumulated threshold T sum is exceeded; if the accumulated values of the previous several merged gray spaces are greater than T sum , the next step of merging in the next gray space is considered; in this way, the original histogram distribution is re-collected and compressed to obtain the compressed histogram statistics hist'(j);

[0014] Step five: histogram statistics on the processed infrared image, and obtaining the following information based on the histogram statistics result: infrared image effective dynamic range histRange', maximum effective gray level histMax', and minimum effective gray level histMin';

[0015] Step six: based on step five, using the mapping mode of the curve to replace the linear straight line mapping, so as to perform nonlinear mapping processing on the infrared image;

[0016] Step seven: linear mapping processing on the infrared image.

[0017] The application also provides a system for implementing the method.

[0018] (Three) beneficial effects

[0019] This invention utilizes the histogram information of the original image and adaptively determines whether to enter the highlight suppression algorithm processing flow based on the statistical results and decision function. In the process, the histogram of the image is collected and compressed, and a nonlinear mapping method is used to effectively suppress the highlight target, so that both the highlight target and the image background are effectively and reasonably displayed on the display device. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention;

[0021] Figure 2 A flowchart for histogram aggregation and compression;

[0022] Figure 3 Comparison of processing effects when highlighting targets entering a scene. Detailed Implementation

[0023] To make the objectives, contents, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0024] This invention provides a scene-adaptive infrared image highlight suppression method, comprising the following steps:

[0025] Step 1: Acquire the raw infrared image;

[0026] Step 2: Perform non-uniform correction and bad pixel replacement on the original infrared image to filter out the impact of bad pixels on histogram statistics;

[0027] Step 3: For simple scenes with a small grayscale range, noise becomes very noticeable due to the large display gain. Therefore, a dynamic range threshold judgment is added to alleviate noise suppression in simple scenes to some extent. For simple scenes with a small grayscale range, there are generally not many unused grayscale levels. Using a wide grayscale image display algorithm would essentially erase many details, compress the grayscale range, and further increase the display gain, making previously inconspicuous noise particularly noticeable. Therefore, it is essential for this algorithm to adaptively determine whether to enter the adaptive highlight suppression algorithm processing flow based on a reasonable decision function. The specific method is as follows: Histogram statistics are performed on the processed infrared image, and the image's nearest upper and lower grayscale values ​​are obtained based on the statistical results. A Schmitt trigger threshold is used to determine whether to enter the adaptive highlight suppression algorithm processing flow. If the activation condition is met, proceed to Step 4; otherwise, proceed to Step 7.

[0028] Step 4: Perform histogram aggregation and compression processing, which aggregates the grayscale pixels to compress the image histogram and free up display space for other grayscale areas.

[0029] In the process of the designed compression histogram implementation, two thresholds are needed to control, one is the gray control threshold T judge , if the gray value statistics is less than the threshold, the next gray value statistics is collected to the current gray level, and the subsequent gray value statistics is forwarded; if the gray value cumulative statistics of the current gray level and the next gray level still does not exceed the cumulative threshold T sum , the next gray value statistics is continued to be collected to the current gray level until the cumulative threshold T sum is exceeded; if the cumulative values of the previous several merged gray spaces are greater than T sum , the next step of merging in the next gray space is considered; and so on, the original histogram distribution is re-collected and compressed to obtain the compressed histogram statistics hist'(j); T judge is generally small, and a value less than 10 is recommended, T sum is the average number of pixels existing in each gray value in the image.

[0030] Step five: histogram statistics is performed on the processed infrared image, and the following information is obtained based on the histogram statistics results: including the infrared image effective dynamic range histRange'(the infrared image effective dynamic range after histogram collection processing), the maximum effective gray level histMax'(the maximum gray value of the image effective after histogram collection processing), and the minimum effective gray level histMin'(the minimum gray value of the image effective after histogram collection processing).

[0031] Step six: the infrared image is subjected to nonlinear mapping processing. The main idea is to use the mapping mode of the curve to replace the linear straight line mapping (the mapping mode of the first straight line), so as to effectively control the suppression effect of the strong target on the image background.

[0032] First, according to the infrared image effective dynamic range histRange' and the gray range Y range of the target mapping threshold, the compression coefficient R is calculated, then the bidirectional mapping curve is obtained according to formula (5) and formula (6), and according to the average gray value avg of the image, the number of pixels less than the average gray value is counted, and the pixel ratio λ of the low gray area is obtained by dividing the statistical number by the image size by using formula (7), finally, the parameter λ is taken as the weighting coefficient, and the final mapping curve is obtained according to formula (8), through which the adaptive nonlinear mapping processing result can be obtained, so as to achieve the purpose of highlight suppression.

[0033] Step seven: if it is judged that the dynamic range does not exceed the opening threshold, the infrared image is subjected to linear mapping processing.

[0034] In step three, the method for determining whether to enter the adaptive highlight suppression algorithm processing flow is as follows: Based on the histogram statistics, calculate the nearest lower limit gray value (histDown) and the nearest upper limit gray value (histUp). For histDown, the gray value is accumulated from the lowest gray value to the highest. When the accumulated value is greater than 0.5% of the total number of pixels in the image, the current gray value is recorded as the nearest lower limit gray value. For histUp, the gray value is accumulated from the highest gray value to the lowest. When the accumulated value is greater than 0.5% of the total number of pixels in the image, the current gray value is recorded as the nearest upper limit gray value.

[0035] The algorithm selects and judges based on the formula, let S(n) represent the judgment result of the current low n frames, where H close To disable the adaptive nonlinear algorithm threshold, H open To enable the adaptive nonlinear algorithm threshold, H close <H open When the difference between the nearest upper limit grayscale value and the nearest lower limit grayscale value is less than H close When S(n) = 0, it indicates that a linear mapping algorithm is being performed; when the difference between the nearest upper limit gray value and the nearest lower limit gray value is greater than H... open When S(n) = 1, it indicates that a nonlinear mapping algorithm is being performed. When the gray values ​​near the upper limit and the lower limit are within H... close With H open In between, the selection state of the previous frame is maintained, as shown in equation (1) below. This type of Schmitt triggering avoids the phenomenon of image flickering caused by the image fluctuating just around a single threshold.

[0036]

[0037] In step four, calculate T. sum The formula is

[0038]

[0039] In the formula: W is the image width (in pixels);

[0040] H is the image height (in pixels);

[0041] X range It represents the grayscale range of the original image (unitless).

[0042] 1) Starting from the lowest gray level, determine whether the statistical pixel value of the original gray level is less than the gray level control threshold T. judge ;

[0043] 2) If the original grayscale level's statistical pixel value is greater than the grayscale control threshold, then the new grayscale level is incremented by one, and the number of pixels is the same as the number of pixels in that grayscale level; if the original grayscale level's statistical pixel value is less than the grayscale control threshold T...judge From this gray level, the cumulative sum is calculated step by step backward, when the cumulative sum exceeds T sum , the new gray level is added by one, and the pixel number is the cumulative sum pixel number;

[0044] 3) The gray levels are compared from small to large step by step according to the principles of 1) and 2), and finally the new histogram statistical result after histogram collection compression is obtained.

[0045] In step six, the formula for calculating the compression coefficient R is as follows:

[0046]

[0047] Y range is the gray range of the target mapping threshold, if it is 8-bit display, Y range is 256, if it is 10-bit display, Y range is 1024; the compression coefficient R can be adjusted adaptively according to the range of the processed image.

[0048] Since the curve model Y1 cannot compress the distribution of the low gray level area, we perform reverse mapping on the model to obtain another mapping curve model Y2. Through the curve model Y1, the compression of the high gray level area and the stretching of the low gray level area are realized; through the mapping curve model Y2, the compression of the low gray level area and the stretching of the high gray level area are realized. Therefore, according to the pixel number ratio of the high gray level area and the low gray level area decomposed above, the weighted average of Y1 and Y2 is obtained to obtain a new mapping curve Y, which becomes the final implementation form. The specific implementation form of the model is as follows:

[0049]

[0050]

[0051] In the formula, Y1 is the forward mapping result, Y2 is the reverse mapping result, and x is the original image gray level. After obtaining the two mapping curve models, the proportion of the low gray level area divided by the gray level mean is calculated as λ; according to the average gray level avg of the image, the pixel number less than the average gray level is counted, and the pixel number is divided by the image size to obtain λ, and the calculation formula is as follows:

[0052]

[0053] In the formula: hist'(j) is the new histogram distribution after histogram collection compression processing, which represents the statistical number of gray level j, represents the sum of the statistical number of gray levels below the mean;

[0054] Finally, the parameter λ is taken as a weighting coefficient, and a final mapping curve is obtained according to formula (8). Through the mapping curve, the processing result of the adaptive nonlinear mapping can be obtained, so as to achieve the purpose of highlight suppression.

[0055] Y = λY1 + (1-λ)(Y range -Y2) … … (8)

[0056] In step seven, if it is judged that the dynamic range does not exceed the opening threshold, linear mapping processing is performed according to formula (9) and formula (10), and overflow protection is performed on the calculation result.

[0057] Y = gain(x-avg) + Y range / 2 … … (9)

[0058]

[0059] In order to verify the applicability of the algorithm to the image display of high-temperature targets, the processing effect of the algorithm is compared to a certain extent for some typical scenes. The images before and after optimization are compared, the comparison of high-temperature objects entering the scene and low-temperature objects entering the scene is compared, and the histogram of the image change and the comparison of the mapping curves before and after optimization are given. Thus, the feasibility of the algorithm and the comparison before and after optimization of the algorithm are verified.

[0060] The method of the present application utilizes the histogram information of the original image to reasonably adjust the histogram of the image, and determines whether the highlight suppression algorithm needs to be started through the histogram information, so as to achieve the purpose of effectively suppressing the highlights in a wide temperature range. Whether to enable the wide gray image display algorithm is determined according to the difference between the adjacent lower limit gray value histDown and the adjacent upper limit gray value histUp, a Schmidt trigger type trigger design is set, and the phenomenon of image flicker caused by algorithm switching near a single threshold is also avoided.

[0061] The adaptive nonlinear mapping curve model of the algorithm is an inverse S-shaped curve, which can better utilize the limited display space of the display device and fully display the suppressed image details. Through the processing comparison of typical scene images, the correctness and applicability of the algorithm model are verified, so that the highlights and the image background can be effectively and hierarchically displayed in the display device under different scenes.

[0062] The above only describes the preferred embodiments of the present application, and it should be noted that, for those skilled in the art, without departing from the technical principles of the present application, a number of improvements and modifications can be made, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. A method for adaptive highlight target rejection in infrared images with a wide dynamic range, characterized in that, The method comprises the following steps: Step one: obtaining an original infrared image; Step two: performing non-uniform correction and bad cell replacement processing on the original infrared image; Step three: performing histogram statistics on the processed infrared image, and obtaining information of a near upper limit gray value and a near lower limit gray value of the image according to the statistical result, and judging whether to enter an adaptive highlight suppression algorithm processing flow by a Schmidt trigger type threshold value, if the opening condition is met, entering step four, and if not, entering step seven; Step four: performing histogram collection compression processing on the infrared image, and giving up image display space for other gray regions; The implementation of the compressed histogram design requires two thresholds for control: one is the grayscale control threshold T. judge If the grayscale value statistic is less than this threshold, the grayscale value statistic of the next level is aggregated into the current grayscale level, and the subsequent grayscale value statistic is advanced forward; if the cumulative grayscale value statistic of the current grayscale level and the next grayscale level still does not exceed the cumulative threshold T, then... sum Then, the statistics of the next level of grayscale value will continue to be collected into the current grayscale level until the accumulation threshold T is exceeded. sum If the cumulative value of the previous merged grayscale spaces is greater than T sum If the result is not found, then consider performing the next merging step in the next grayscale space; and so on, re-aggregate and compress the original histogram distribution to obtain the compressed histogram statistics hist′(j); Step five: performing histogram statistics on the processed infrared image, and obtaining the following information based on the histogram statistical result: an effective dynamic range histRange' of the infrared image, a maximum effective gray level histMax', and a minimum effective gray level histMin'; Step six: based on step five, using a mapping mode of a curve to replace linear straight line mapping, so as to perform non-linear mapping processing on the infrared image; Step seven: performing linear mapping processing on the infrared image; In step three, the method for judging whether to enter the adaptive highlight suppression algorithm processing flow is as follows: according to the histogram statistical result, a near lower limit gray value histDown and a near upper limit gray value histUp are calculated respectively, the histDown needs to be accumulated and counted from the lowest gray value from low to high, and when the accumulated value is greater than 0.5% of the total pixel quantity, the current gray value is recorded as the near lower limit gray value; the histUp needs to be accumulated and counted from the highest gray value from high to low, and when the accumulated value is greater than 0.5% of the total pixel quantity, the current gray value is recorded as the near upper limit gray value. Let S(n) represent the decision result of the current low n frame, wherein H close is the closing adaptive nonlinear algorithm threshold value, H open is the opening adaptive nonlinear algorithm threshold value, H close＜ is the closing adaptive linear algorithm threshold value, H open ; when the difference between the adjacent upper limit gray value and the adjacent lower limit gray value is less than H close , S(n)=0, indicating that the linear mapping algorithm is performed; when the difference between the adjacent upper limit gray value and the adjacent lower limit gray value is greater than H open , S(n)=1, indicating that the nonlinear mapping algorithm is performed; when the adjacent upper limit gray value and the adjacent lower limit gray value are between H close and H open , the selection state of the previous frame is maintained, as shown in the following formula (1):

2. The method of claim 1, wherein, In step four, T is calculated sum The formula is where: W is the image width; H is the image height; X range is the gray scale range of the original image; 1) From the minimum gray level, determine whether the gray level statistical pixel value of the original image is less than the gray control threshold T judge ; 2) If the pixel value of the original image is greater than the gray control threshold, the new gray level is increased by one, and the pixel number is the number of pixels of the gray level; if the pixel value of the original image is less than the gray control threshold T judge , the new gray level is increased by one, and the pixel number is the number of pixels of the gray level; if the pixel value of the original image is less than the gray control threshold T sum , the new gray level is increased by one, and the pixel number is the number of pixels of the gray level; if the pixel value of the original image is less than the gray control threshold T sum , the new gray level is increased by one, and the pixel number is the number of pixels of the gray level; if the pixel value of the original image is less than the gray control threshold T sum , the new gray level is increased by one, and the pixel number is the number of pixels of the gray level; if the pixel value of the original image is less than the gray 3) comparing the gray values from small to large step by step according to the principles of 1) and 2), and finally obtaining the new histogram statistical result after the histogram collection compression.

3. The method of claim 2, wherein, In step six, first according to the infrared image effective dynamic range histRange' and the target mapping threshold gray scale range Y range Calculate the compression coefficient R; Then, a bidirectional mapping curve is obtained, the number of pixels less than the average gray value of the image is counted according to the average gray value avg of the image, the pixel ratio λ of the low gray region is obtained by dividing the statistical number by the image size, and finally the parameter λ is taken as a weighting coefficient to obtain the final mapping curve, and the processing result of the adaptive non-linear mapping is obtained through the mapping curve.

4. The method of claim 3, wherein, In step six, the formula for calculating the compression coefficient R is as follows: Y range The gray scale range that is targeted for the threshold.

5. The method of claim 4, wherein, In step six, the bidirectional mapping curve is obtained according to formula (5) and formula (6): In the formula, Y1 is the forward mapping result, Y2 is the inverse mapping result, and x is the original image gray value.

6. The method of claim 5, wherein, In step six, the number of pixels less than the average gray value of the image is counted according to the average gray value avg of the image, and the pixel ratio λ of the low gray region is obtained by dividing the statistical number by the image size according to formula (7): In the formula, hist'(j) is the new histogram distribution after histogram compression, and represents the statistical quantity of gray level j, represents the sum of the statistical quantities of the gray levels below the mean.

7. The method of claim 6, wherein, In step six, the parameter λ is taken as a weighting coefficient, the final mapping curve is obtained according to formula (8), and the processing result of the adaptive non-linear mapping is obtained through the mapping curve: Y = λY1+ (1 - λ) (Y range - Y2) (8).

8. The method of claim 7, wherein, In step seven, linear mapping processing is performed according to formula (9) and formula (10), and overflow protection is performed on the calculation result. Y = gain(x - avg) + Y range / 2 … … (9)

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