CN110570374A - Processing method for image obtained by infrared sensor - Google Patents
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- YOBAEOGBNPPUQV-UHFFFAOYSA-N iron;trihydrate Chemical compound O.O.O.[Fe].[Fe] YOBAEOGBNPPUQV-UHFFFAOYSA-N 0.000 claims description 3
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The invention provides a processing method of an image obtained by an infrared sensor, which comprises four processing parts of detail enhancement, light modulation, denoising and pseudo color, wherein the detail enhancement refers to the enhancement of detail information in the image, the dimming processes the nonlinear mapping of original data and compresses the size of the original data, then the denoising filters the noise information in the image, and finally the pseudo color is used for carrying out color mapping on the processed gray level image and converting the gray level image into a color image; the invention uses new methods of unilateral gray filtering, denoising, new iron oxide red pseudo color and the like, thereby carrying out smooth processing on the gray of the edge pixel point of the image to obtain the image with less faults, not losing too much details while denoising, keeping the integrity of the image, particularly protecting the edge of the image to obtain higher recovery effect, and also leading an observer to have more vivid color level distinction when observing a high-temperature object, thereby being capable of more easily and accurately determining the position of a heat source.
Description
Technical Field
the invention relates to the technical field of image processing, in particular to a method for processing an image acquired by an infrared sensor.
Background
the infrared imaging electronic component is an important component of a thermal imaging system, and has the function of fully playing the performance of an infrared focal plane detector and converting an electric signal output by the infrared focal plane detector into a video signal or a signal in a format specified by other systems after processing. The infrared imaging electronic component comprises two parts, namely a hardware system and an image processing algorithm, and because the current hardware system platform is very complete, the infrared image processing technology becomes an important research content of the imaging electronic component.
The infrared image processing technology needs to be developed for specific characteristics of infrared images. The method is limited by the manufacturing process difficulty and material purity influence of the infrared detector, and the infrared image mainly has the following common problems: firstly, the infrared imaging is affected by non-uniformity and invalid pixels, the actual resolution is not high, and particularly, the detail part of the image is not fine enough; secondly, the infrared imaging generally has the problem of large noise; second, infrared imaging mainly uses "iron red" standard pseudo-color at present, but the color distribution gradation of this pseudo-color at the high temperature part is not obvious enough. Therefore, an infrared image processing method with higher detail resolution, cleaner image and more vivid color gradation is needed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a processing method of an image obtained by an infrared sensor, which is used for solving the problems of insufficient projection of image details, large noise and insufficient vividness of colors at high-temperature parts in the prior infrared image processing technology.
In order to achieve the purpose, the invention adopts the following technical scheme:
a processing method for an image obtained by an infrared sensor comprises four processing parts of detail enhancement, light modulation, denoising and pseudo color, wherein the detail enhancement refers to the enhancement of detail information in the image, so that the image details are more prominent and the edges are sharper, the original data are subjected to nonlinear mapping and the size of the original data is compressed when the light modulation is carried out, then the noise information in the image is filtered through denoising, so that the image looks cleaner, and finally the processed gray image is subjected to color mapping by using the pseudo color and is converted into a color image;
the specific processing steps of the detail enhancement are as follows:
(1) unilateral gray level filtering: selecting a mask for an original image img0, taking the same value of the weight of the spatial distance of each pixel point in the mask in the bilateral filtering processing method, only considering the weighting influence of the gray difference weight in the mask on the current pixel point, obtaining a weight table through Gaussian function calculation according to the set standard difference and the window size, inquiring the corresponding weight value in the weight value by using the gray difference absolute value as an index value in the filtering process of traversing all the pixel points, then carrying out weighted summation on all the pixel points in the mask, simultaneously carrying out summation on the weight values, and finally carrying out normalization to obtain the filtering result img1 of the current pixel;
(2) Gaussian filtering: performing conventional distance domain Gaussian filtering on the image obtained by the unilateral gray level filtering again, processing under the conditions that the standard deviation is the same as that in the step (1) and the window size is smaller than that in the step (1), filtering excessive detail information, and outputting to obtain a low-frequency image img 2;
(3) high frequency limit: subtracting the low-frequency image from the original image img0 to obtain a high-frequency image img3, and then carrying out limit value processing on each pixel point;
(4) low-frequency dimming: dimming processing is carried out on the Gaussian filtered low-frequency image img2, and an img4 is obtained by selecting a proper dimming algorithm;
(5) High-low frequency fusion: summing the low frequency image img4 and the high frequency image img3 to obtain a resultant image;
The denoising comprises the following steps:
the method comprises the following steps: establishing a window S of (2n +1) × (2n +1) for the image, calculating the difference value of corresponding pixel points in the windows of the two frames of images before and after, and then averaging, namely:
Traversing each pixel point in the image by the operation;
step two: calculating the Gaussian kernel weight of the pixel point:
Step three: calculating a filtered image:
preferably, when the weight table is calculated using the gaussian function in step (1), the set standard deviation gray _ std is 20, and the window size used is 512.
preferably, when the gaussian filter calculation is performed in step (2), the standard deviation gray _ std is set to 20, and the window size used is 3 × 3.
Preferably, a linear dimming algorithm is selected in the step (4).
preferably, the data format after the limiting value processing and the dimming processing in the step (3) and the step (4) is int 16.
further, the specific calculation method of the image summation in the step (5) is as follows:
Wherein nDetailGain is the gain coefficient of the detail image.
Preferably, the dynamic range of the input original image img0 is 16 bits, and the output data after processing is 8 bits.
preferably, a flat histogram algorithm is used in the dimming processing.
further, the false color is a new iron oxide red false color which increases the color distribution of the high-temperature section for the existing iron oxide red false color.
Further, in the step (5), since the nDetailGain is 16 times larger than the actual value for the fix-point operation, the nDetailGain needs to be reduced to the original value after the multiplication is completed.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, a high-frequency and low-frequency separation mode is adopted for detail enhancement, firstly, original data are subjected to a low-pass filter to obtain low-frequency information, then the original data and the low-frequency information are subtracted to obtain high-frequency information, the high-frequency information is subjected to adaptive gain processing, and then the high-frequency information and the low-frequency information are added to obtain an image with the enhanced details;
2. the method uses unilateral gray filtering in detail enhancement, and determines the position and the gray value of a central point after convolution of a weight coefficient kernel of the gray and an image under the condition of the same spatial position weight, thereby performing smooth processing on the gray of edge pixel points of the image and obtaining the image with fewer faults;
3. in the invention, the difference value between the front frame and the rear frame of the pixel point in the set window is calculated and then averaged, and the difference value is taken as a constant coefficient through a Gaussian kernel value and is brought into a filtering image, so that the noise point is screened out in the calculation process; the denoising processing method in the embodiment combines the mean filtering and the median filtering, so that excessive details are not lost while denoising is performed, the integrity of the image is kept, particularly the edge of the image is protected, and a higher restoration effect is obtained;
4. The novel iron oxide red pseudo-color is adopted, and is correspondingly improved aiming at the application characteristics of the power transformation detection based on the 'iron oxide red' standard pseudo-color commonly used in the industry.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a detailed flow diagram of the detail enhancement section of the present invention;
Detailed Description
the technical solution of the present invention is further explained with reference to the drawings and the embodiments.
Example (b):
as shown in FIG. 1, the invention provides a method for processing an image obtained by an infrared sensor, comprising four processing parts of detail enhancement, dimming, denoising and pseudo-color, wherein the detail enhancement refers to the enhancement of detail information in the image, so that the image details are more prominent and the edges are sharper, the dimming performs nonlinear mapping on original data and compresses the size of the original data, then the denoising filters noise information in the image, so that the image looks cleaner, and finally the pseudo-color is used for performing color mapping on a processed gray level image, so that the gray level image is converted into a color image, and finally the original image is converted into the color image which can visually reflect the temperature state distribution. Preferably, a platform histogram algorithm is used in the dimming processing, and in this embodiment, the dynamic range of the input original image img0 is 16 bits, and the output data after processing is 8 bits.
As shown in fig. 2, the specific processing steps for detail enhancement are as follows:
(1) unilateral gray level filtering: selecting a mask for an original image img0, taking the same value of the weight of the spatial distance of each pixel point in the mask in the bilateral filtering processing method, only considering the weighted influence of the gray difference weight in the mask on the current pixel point, obtaining a weight table through Gaussian function calculation according to the set standard deviation and the window size, inquiring the corresponding weight value in the weight value by using the gray difference absolute value as an index value in the filtering process of traversing all the pixel points, then carrying out weighted summation on all the pixel points in the mask, simultaneously carrying out summation on the weight values, and finally carrying out normalization to obtain the filtering result img1 of the current pixel. Preferably, the set standard deviation gray _ std is 20, and the window size used is 512.
In the bilateral filtering algorithm:
The Gaussian kernel function of spatial distance iswherein (x)c,yc) Is a center point coordinate, such as (0,0), (x)i,yi) The coordinate of the current point is, and sigma is a space domain standard deviation;
the Gaussian kernel function of the gray scale distance iswherein gray (x)i,yi) Is the gray value, gray (x) of the current pixel pointc,yc) Is the gray value of the pixel at the center point of the template covering the picture area, i.e. the gray value at (0,0), and σ is the value range labelAnd (4) tolerance.
for Gaussian filtering, the gray value of the central point is determined only by convolving the weight coefficient kernel of the spatial distance with the image. I.e. the closer to the center point, the larger its weight coefficient. The weight of the gray information is added in the bilateral filtering, namely in the field, the more the gray value is close to the point of the gray value of the central point, the more the gray value is, the smaller the weight of the point with large gray value difference is, and the weight is determined by a value domain Gaussian function. And multiplying the two weight coefficients to obtain a final convolution template, namely the processed result image.
In the unilateral gray level filtering algorithm, the spatial weights of all pixel points in the mask are regarded as the same, namely the Gaussian kernel function of the spatial distance is 0, namely the finally changed actual weighting coefficient isIn this way, when the gray value is considered, the image is subjected to filtering processing, and the gray value change of the edge part is reserved, so that the image result that the gray value transition is more moderate and the cliff region does not exist is obtained. Accordingly, the calculation process of the filtering is greatly simplified because the space weight part is abandoned, and the processing speed of the image is improved.
(2) gaussian filtering: performing conventional distance domain Gaussian filtering on the image obtained by the unilateral gray level filtering again, processing under the conditions that the standard deviation is the same as that in the step (1) and the window size is smaller than that in the step (1), filtering excessive detail information, and outputting to obtain a low-frequency image img 2; preferably, the standard deviation gray _ std is set to 20, and the window size used is 3 × 3.
(3) High frequency limit: subtracting the low-frequency image from the original image img0 to obtain a high-frequency image img3, and then carrying out limit value processing on each pixel point;
(4) low-frequency dimming: dimming processing is carried out on the Gaussian filtered low-frequency image img2, and an img4 is obtained by selecting a proper dimming algorithm; preferably, a linear dimming algorithm is used in the present embodiment. And the format of the data after the limit value processing and the dimming processing in the step (3) and the step (4) is int 16.
(5) High-low frequency fusion: the low frequency image img4 and the high frequency image img3 are summed to obtain a resultant image. The specific calculation method is as follows:
The nDetailGain is a gain coefficient of the detail image, and is further amplified by 16 times compared with an actual value, so that the nDetailGain needs to be reduced to an original multiple after the multiplication operation is finished.
According to the method, a high-frequency and low-frequency separation mode is adopted for detail enhancement, firstly, original data are subjected to a low-pass filter to obtain low-frequency information, then the original data and the low-frequency information are subtracted to obtain high-frequency information, the high-frequency information is subjected to adaptive gain processing, and then the high-frequency information and the low-frequency information are added to obtain an image with the enhanced details; and then, using unilateral gray level filtering, and determining the position of the central point and the gray level value only by convolution of a weight coefficient kernel of the gray level and the image under the condition of the same spatial position weight, thereby performing smooth processing on the gray level of the edge pixel point of the image to obtain the image with fewer faults, and further processing the infrared gray level image to be more suitable for human eye detection.
Further, the denoising process includes the following steps:
The method comprises the following steps: establishing a window S of (2n +1) × (2n +1) for the image, calculating the difference value of corresponding pixel points in the windows of the two frames of images before and after, and then averaging, namely:
traversing each pixel point in the image by the operation;
step two: calculating the Gaussian kernel weight of the pixel point:
step three: calculating a filtered image:
The filtered image In(x, y), and obtaining a final result image after pseudo-color processing.
It should be noted that, because human eyes are more sensitive to color information, the pseudo color refers to a process of performing color mapping on a processed 8bit gray image and converting the processed 8bit gray image into a color image. Since the infrared image itself is a single channel, with no color information, it is called "pseudo color" or "false color". In the invention, the pseudo color is a new iron oxide red pseudo color generated after corresponding improvement on the basis of an iron oxide red standard pseudo color commonly used in the industry and aiming at the application characteristics of power transformation detection. The new iron oxide red pseudo-color increases the color distribution in the high-temperature section, so that an observer can more easily and accurately determine the position of the heat source when observing a high-temperature object.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (10)
1. a method for processing images obtained by an infrared sensor is characterized in that: the method comprises four processing parts of detail enhancement, light modulation, denoising and pseudo color, wherein the detail enhancement refers to the enhancement processing of detail information in an image, so that the image details are more prominent and the edges are sharper, the light modulation is used for carrying out nonlinear mapping on original data and compressing the size of the original data, then the noise information in the image is filtered through denoising, so that the image looks cleaner, and finally the pseudo color is used for carrying out color mapping on a processed gray level image and converting the gray level image into a color image;
the specific processing steps of the detail enhancement are as follows:
(1) Unilateral gray level filtering: selecting a mask for an original image img0, taking the same value of the weight of the spatial distance of each pixel point in the mask in the bilateral filtering processing method, only considering the weighting influence of the gray difference weight in the mask on the current pixel point, obtaining a weight table through Gaussian function calculation according to the set standard difference and the window size, inquiring the corresponding weight value in the weight value by using the gray difference absolute value as an index value in the filtering process of traversing all the pixel points, then carrying out weighted summation on all the pixel points in the mask, simultaneously carrying out summation on the weight values, and finally carrying out normalization to obtain the filtering result img1 of the current pixel;
(2) Gaussian filtering: performing conventional distance domain Gaussian filtering on the image obtained by the unilateral gray level filtering again, processing under the conditions that the standard deviation is the same as that in the step (1) and the window size is smaller than that in the step (1), filtering excessive detail information, and outputting to obtain a low-frequency image img 2;
(3) High frequency limit: subtracting the low-frequency image from the original image img0 to obtain a high-frequency image img3, and then carrying out limit value processing on each pixel point;
(4) low-frequency dimming: dimming processing is carried out on the Gaussian filtered low-frequency image img2, and an img4 is obtained by selecting a proper dimming algorithm;
(5) high-low frequency fusion: summing the low frequency image img4 and the high frequency image img3 to obtain a resultant image;
the denoising comprises the following steps:
The method comprises the following steps: establishing a window S of (2n +1) × (2n +1) for the image, calculating the difference value of corresponding pixel points in the windows of the two frames of images before and after, and then averaging, namely:
Traversing each pixel point in the image by the operation;
step two: calculating the Gaussian kernel weight of the pixel point:
step three: calculating a filtered image:
2. a method of processing images obtained by an infrared sensor, as claimed in claim 1, characterized in that: when the weight table is calculated using the gaussian function in step (1), the set standard deviation gray _ std is 20, and the window size used is 512.
3. A method of processing images obtained by an infrared sensor, as claimed in claim 1, characterized in that: when the gaussian filtering calculation is performed in the step (2), the set standard deviation gray _ std is 20, and the window size used is 3 × 3.
4. A method of processing images obtained by an infrared sensor, as claimed in claim 1, characterized in that: the linear dimming algorithm is selected in the step (4).
5. A method of processing images obtained by an infrared sensor, as claimed in claim 1, characterized in that: the data format after the limiting value processing and the dimming processing in the step (3) and the step (4) is int 16.
6. A method of processing images obtained by an infrared sensor, as claimed in claim 1, characterized in that: the specific calculation method of the image summation in the step (5) is as follows:
wherein nDetailGain is the gain coefficient of the detail image.
7. a method of processing images obtained by an infrared sensor, as claimed in claim 1, characterized in that: the dynamic range of the input original image img0 is 16 bits, and the output data after processing is 8 bits.
8. A method of processing images obtained by an infrared sensor, as claimed in claim 1, characterized in that: the dimming processing uses a platform histogram algorithm.
9. A method of processing images obtained by an infrared sensor, as claimed in claim 1, characterized in that: the pseudo color is a new iron-red pseudo color which increases the color distribution of the high-temperature section for the existing iron-red pseudo color.
10. A method of processing images obtained by an infrared sensor, as claimed in claim 6, characterized in that: in the step (5), the nDetailGain is enlarged by 16 times compared with the actual value for the fixed-point operation, and therefore, the nDetailGain needs to be reduced to the original multiple after the multiplication is finished.
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