CN103335721A - Scene matching-based dynamic blind pixel detection method for infrared focal plane array - Google Patents
Scene matching-based dynamic blind pixel detection method for infrared focal plane array Download PDFInfo
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Abstract
The invention belongs to the technical field of image processing in an infrared focal plane array imaging technology, and particularly relates to an infrared focal plane array dynamic blind pixel detection method based on scene matching. The method comprises the steps of firstly, comparing a relative error of a pixel response value of a sliding window with a preset threshold value, detecting potential blind pixels existing in the window, and finally, matching the processing results of two frames of scene images to eliminate false blind pixels and determine real blind pixels. The method solves the problems that the currently common calibration and scene infrared focal plane array dynamic blind pixel detection method has limitation and low accuracy.
Description
Technical Field
The invention belongs to the technical field of image processing in an infrared focal plane array imaging technology, and particularly relates to an infrared focal plane array dynamic blind pixel detection method based on scene matching.
In the background art, in the prior art,
with the continuous development of Infrared imaging technology and the increasing maturity of Infrared focal plane Array (IRFPA) image sensors, the IRFPA is widely applied to various thermal imaging systems in the fields of military, industry, commerce and the like. Due to manufacturing techniques, manufacturing processes, and raw materials, there is typically a non-uniformity of response between detector elements of the infrared focal plane array. The extremes of non-uniformity are: when the incident radiation changes, the response of some detector elements is always too high or too low, resulting in bright or dark spots on the image that affect the visual effect, i.e. blind elements (also called nulls). The quality of the image is reduced due to the existence of the blind pixels, and subsequent processing such as non-uniform correction, image enhancement, target detection and identification is influenced. Therefore, the blind pixels are detected and compensated by using an advanced image processing technology, and the method has an important application value for improving the imaging quality of the IRFPA.
The processing of the blind pixels comprises two aspects: firstly, blind pixel detection is carried out, and the specific position of the blind pixel is determined; then blind pixel compensation is carried out, and the response value of the blind pixel is replaced by the approximate reasonable value. The blind pixel detection is a precondition and a basis for blind pixel compensation. Various blind pixel detection algorithms are presented at present at home and abroad, and the algorithms are mainly divided into two categories: one is a detection method based on blackbody calibration, and the other is a detection method based on a scene. The method based on black body calibration is most widely applied to various IRFPA imaging systems at present, and the basic principle is that a black body is utilized to obtain a uniform radiation image, and on the basis, a blind element and a normal detection element are distinguished according to the difference between the blind element and the normal detection element in the aspects of response characteristics, noise characteristics and the like. The blind pixel detection algorithm based on blackbody calibration is simple in principle, but requires the matching of blackbodies, cannot process new blind pixels randomly appearing in practical application due to the change of environmental temperature, and is low in detection efficiency. The scene detection method does not depend on additional equipment, can effectively correct the inherent blind pixels of the IRFPA and the random blind pixels generated due to the change of the ambient temperature, and has higher detection efficiency and better environmental adaptability.
The commonly used calibration-type blind pixel detection methods include the following methods:
(1) defining the identification method: according to the definition of the national standard [1], the pixels with response values lower than 1/10 and higher than 10 times of threshold values are marked as blind pixels by taking 10 times of the average response rate of all pixels in the image and 1/10 as the critical threshold values for blind pixel detection.
(2) Double reference radiation source method: an infrared focal plane array is used for imaging the high-temperature and low-temperature black body irradiation sources [2], and the difference value of the response of the detector to the high-temperature and low-temperature black bodies and the average response difference value of the pixels are obtained. And if the response difference is more than 10 times of the average response difference or less than 1/10 of the average response difference, the image element is considered as a blind pixel.
(3)3 σ assay: according to the method, under the irradiation of a uniform black body with the temperature of T, the response of an infrared focal plane array detection element and the noise thereof follow normal distribution:
Φ(x)=(2πσ)^(-1/2)exp[-(x-μ)^2/(2σ^2)]
wherein mu is the response mean value of the detection element, sigma is the mean square error of the response value, and if the response value of a certain detection element exceeds mu +/-3 sigma, the pixel is considered as a blind element.
At present, a scene-based blind pixel detection method mainly comprises a bilinear extrapolation method and a background prediction method. Bilinear extrapolation [ 3 ] adopts median filtering based on linear extrapolation to realize the detection and compensation of blind pixels. A background prediction method (4) decomposes an infrared scene image into blind pixels and a background, extracts the background through a background prediction model, and then realizes image blind pixel detection by using the difference between the blind pixels and normal pixels. Both methods are based on blind pixel detection of a single-frame scene image, the median filtering algorithm adopted by the former method can filter some useful weak signals, the detection accuracy of the latter method depends on the accuracy of a background prediction model, and errors of the background prediction model can cause part of signals to be mistakenly considered as blind pixels.
Document [1]
GB/T17444-1998. Provided is an infrared focal plane array parameter testing method.
Document [2]
Zhou Huo Xin, Yin Shi Min, Liu Shang Qian, etc. Infrared focal plane device blind pixel detection and compensation algorithm [ J ] & ltProcter photonics, 2004, 33 (5): 598-600.
Document [ 3 ]
Lihuaqiong, Chenqian, Gaowen. Dynamic detection and correction calculation of failed elements of infrared focal plane array [ J ], [ infrared and laser engineering, 2006, 35 (2): 192-196.
Document [ 4 ]
Huang xi, Zhang Jianqi and Liu De Lian. An infrared image blind pixel self-adaptive detection and compensation algorithm [ J ]. Infrared and laser engineering, 2011,40(2): 370-.
Disclosure of Invention
The invention provides a scene matching-based infrared focal plane array dynamic blind pixel detection method, which solves the problems that the currently common calibration and scene infrared focal plane array dynamic blind pixel detection method has limitation and low accuracy.
The specific technical scheme of the invention is as follows:
the invention provides a scene matching-based infrared focal plane array dynamic blind pixel detection method, which comprises the following steps of:
step 1) acquiring F (F is more than or equal to 50) frame scene image data, defining a sliding window with the size of (2n +1) × (2n +1) in a scene image, and n =1 or n = 2; calculating time domain noise of all pixels in the sliding window so as to obtain an adaptive threshold corresponding to a central pixel of the sliding window, and traversing the sliding window through the whole frame of image data so as to determine the adaptive threshold of the central pixel in each sliding window in the scene image;
the self-adaptive threshold value of the central pixel of the sliding window and the mean value of the time domain noise in the sliding window are in a multiple relation;
step 2) collecting a first frame of scene image in the sliding window, and detecting a blind pixel coordinate matrix of the frame of scene image, wherein the specific steps are as follows;
step 2.1) calculating the sum of all pixel response values in the sliding window by detecting the maximum response value MAX and the minimum response value MIN of each pixel in the sliding window defined in the step 1);
and if the sum of the response values of all the pixels in the sliding window is S, the S is:
wherein X (i, j) is the response value of the central pixel of the sliding window, and p and q respectively represent the coordinate offset of other pixels in the sliding window relative to X (i, j); and-n is equal to or greater than p and equal to or less than n, and-n is equal to or greater than q and equal to or less than n;
step 2.2) preliminarily determining the response value range of the blind pixel according to the maximum response value MAX and the minimum response value MIN of the pixel in the sliding window and the self-adaptive threshold value delta (i, j), calculating the sum of the response values of the normal pixels in the sliding window and the number of the normal pixels, and calculating the average response value of the normal pixels;
the specific calculation method is as follows:
setting the response value of the pixels in the sliding window as X (i, j), the sum of the response values of the normal pixels as S, the number of the normal pixels as C, the average value of the normal pixels as Save, the maximum response value as MAX and the minimum response value as MIN, and the specific calculation mode is as follows:
if the normal pixel number C =0, the average value of all the normal pixel response values in the sliding window is calculated as:
Save=S/(2n+1)2
if the normal pixel number C is not equal to 0, the average value of the response values of the normal pixels in the sliding window is calculated as:
Save=S'/C
step 2.3) calculating the relative error between the response value of all pixels in the sliding window and the average response value of all pixels;
assuming that the relative error of the pixel response value X (i, j) in the sliding window is Δ X (i, j), then:
ΔX(i,j)=|X(i,j)-Save|/Save
and 2.4) comparing the calculated delta X (i, j) with a set threshold value T, if the delta X (i, j) is more than or equal to T, indicating that the pixel is a blind pixel, otherwise, indicating that the pixel is a normal pixel, and setting a corresponding flag bit. And sliding the sliding window of (2n +1) × (2n +1) to enable the sliding window to traverse the whole frame of scene image, and finally determining a blind pixel coordinate matrix of the first frame of scene image.
And 3) acquiring a second frame of image, and repeatedly executing the step 2) to obtain a blind pixel coordinate matrix of the second frame of scene image.
And 4) matching the blind pixel coordinate matrixes of the first frame of scene image and the second frame of scene image to determine the final blind pixel position.
The specific calculation method of the step 1) is as follows:
setting the central pixel response value of f frame in the sliding window as Xf(i, j), the temporal noise of the pixel is defined as σF(i,j)，
Is the average value of all pixel response values in the F frame scene image, then sigmaF(i, j) is:
wherein,
the adaptive threshold value delta (i, j) corresponding to the center pixel of the sliding window is as follows:
wherein,
is the time domain noise sigma of all pixels with X (i, j) as the center in the sliding windowF(i, j), wherein F represents the number of collected frames;
the set threshold value T is an empirical value, and the value range is generally between 0.4 and 0.6.
The invention has the advantages that:
the method extracts potential blind pixels in different scene images by utilizing the sliding window and the self-adaptive threshold value, then matches the positions of the blind pixels to determine the final blind pixel distribution, overcomes the limitation of the current commonly used calibration type and scene type blind pixel detection method, and has good accuracy and adaptability to complex infrared scenes.
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FIG. 1 is a flow chart of an embodiment of the method of the present invention;
Detailed Description
The following detailed description of the implementation of the present invention is made with reference to the accompanying drawings and examples, as shown in fig. 1, and includes the following steps: (1) calculating the time domain noise of all pixels in the sliding window and the self-adaptive threshold of the center pixel of the sliding window; (2) determining a blind pixel position coordinate matrix in the scene images of the first frame and the second frame; (3) and matching the two blind pixel position coordinate matrixes to determine final blind pixel distribution.
The specific calculation method is as follows:
firstly, calculating the time domain noise of all pixels in the sliding window so as to calculate the self-adaptive threshold value of the center pixel of the sliding window, and traversing the whole frame of image data so as to determine the self-adaptive threshold value of the center pixel in each sliding window. The following method for calculating the time domain noise and the adaptive threshold in a sliding window is as follows:
collecting F (F is more than or equal to 50) frame scene data, and setting the time domain noise of the image elements (i, j) of the F frame scene image as sigmaF(i,j)；Xf(i, j) is the response value of the center pixel of the F frame, wherein F is less than or equal to F and greater than or equal to 1;(i, j) is the average value of all pixel response values in the F frame scene image; f represents the total collection frame number;
(i, j) is a sliding windowAll picture element time domain noise sigma in the mouthF(i, j) average value.
The adaptive threshold δ (i, j) within the sliding window is:
secondly, collecting the first frame image in the sliding window S1, detecting the maximum response value MAX and the minimum response value MIN in the sliding window, and solving the sum S of all pixel element response values in the sliding window.
Generally, considering that a plurality of blind pixels may exist in the sliding window and the response values of the pixels are not necessarily equal, the sum S' of the response values of the normal pixels and the number C of the normal pixels should satisfy the following two conditions;
if the condition [1] is satisfied, X (i, j) is indicated as a blind pixel, the response value of the pixel is subtracted from S, and accordingly the number of normal pixels in the sliding window is subtracted by 1, and if the condition [2] is satisfied, X (i, j) is indicated as a normal pixel. After traversing all the pixels in the sliding window, the average value of the normal pixels in the sliding window can be finally determined.
If the normal pixel number C =0, the average value of all pixels in the sliding window is calculated as
Save=S/(2n+1)2
If the normal pixel number C is not equal to 0, the average value of the normal pixels in the sliding window is calculated to be
Save=S'/C
The relative error of the central pixel X (i, j) of the window from the average Save, i.e. the error
ΔX(i,j)=|X(i,j)-Save|/Save
And (3) comparing the delta X (i, j) with a set threshold value T (the T value is an empirical value and can be between 0.4 and 0.6). And comparing, if the delta X (i, j) is more than or equal to T, the pixel is represented as a blind pixel, otherwise, the pixel is represented as a normal pixel, and a corresponding flag bit is set. The (2n +1) × (2n +1) sliding window is slid through the entire frame of scene images, and finally the blind-pixel coordinate matrix B1(i, j) of the first frame of scene image S1 is determined. In B1(i, j), the coordinate (i, j) flag corresponding to the blind pixel is 1, and the coordinate (i, j) flag corresponding to the normal pixel is 0.
And acquiring a second frame scene image S2, and executing the blind pixel detection algorithm to obtain a blind pixel coordinate matrix B2(i, j) of the second frame scene image S2. In B2(i, j), the coordinate (i, j) flag corresponding to the blind pixel is 1, and the coordinate (i, j) flag corresponding to the normal pixel is 0.
And matching the blind pixel coordinate matrixes B1(i, j) and B2(i, j) of the scene images acquired twice, and determining the coordinate (i, j) of which the B1(i, j) and the B2(i, j) are 1 at the same time as the final blind pixel coordinate.
Then, blind pixel compensation is performed on the finally determined blind pixels, and the output of the blind pixels is generally replaced by the average value of normal pixels in a sliding window of (2n +1) × (2n + 1).
Claims (3)
1. A scene matching-based dynamic blind pixel detection method for an infrared focal plane array is characterized by comprising the following steps: the method comprises the following steps:
1) collecting F (F is more than or equal to 50) frame scene image data, defining a sliding window with the size of (2n +1) × (2n +1) in a scene image, and n =1 or n = 2; calculating time domain noise of all pixels in the sliding window so as to obtain an adaptive threshold corresponding to a central pixel of the sliding window, and traversing the sliding window through the whole frame of image data so as to determine the adaptive threshold of the central pixel in each sliding window in the scene image;
the self-adaptive threshold value of the central pixel of the sliding window and the mean value of the time domain noise in the sliding window are in a multiple relation;
2) collecting a first frame of scene image in a sliding window, and detecting a blind pixel coordinate matrix of the frame of scene image, wherein the method comprises the following specific steps;
2.1) solving the sum of all pixel element response values in the sliding window by detecting the maximum response value MAX and the minimum response value MIN of each pixel element in the sliding window defined in the step 1);
and if the sum of the response values of all the pixels in the sliding window is S, the S is:
wherein X (i, j) is the response value of the central pixel of the sliding window, and p and q respectively represent the coordinate offset of other pixels in the sliding window relative to X (i, j); and-n is equal to or greater than p and equal to or less than n, and-n is equal to or greater than q and equal to or less than n;
2.2) preliminarily determining the response value range of the blind pixel according to the maximum response value MAX and the minimum response value MIN of the pixel in the sliding window and the self-adaptive threshold value delta (i, j), calculating the sum of the response values of the normal pixels in the sliding window and the number of the normal pixels, and calculating the average response value of the normal pixels;
the specific calculation method is as follows:
setting the response value of the pixels in the sliding window as X (i, j), the sum of the response values of the normal pixels as S, the number of the normal pixels as C, the average value of the normal pixels as Save, the maximum response value as MAX and the minimum response value as MIN, and the specific calculation mode is as follows:
if the normal pixel number C =0, the average value of all the normal pixel response values in the sliding window is calculated as:
Save=S/(2n+1)2
if the normal pixel number C is not equal to 0, the average value of the response values of the normal pixels in the sliding window is calculated as:
Save=S'/C
2.3) calculating the relative error between the response value of all pixels in the sliding window and the average response value of all pixels;
assuming that the relative error of the pixel response value X (i, j) in the sliding window is Δ X (i, j), then:
ΔX(i,j)=|X(i,j)-Save|/Save
2.4) comparing the calculated delta X (i, j) with a set threshold value T, if the delta X (i, j) is more than or equal to T, indicating that the pixel is a blind pixel, otherwise, indicating that the pixel is a normal pixel, and setting a corresponding flag bit. And sliding the sliding window of (2n +1) × (2n +1) to enable the sliding window to traverse the whole frame of scene image, and finally determining a blind pixel coordinate matrix of the first frame of scene image.
3) And (3) collecting a second frame of image, and repeatedly executing the step 2) to obtain a blind pixel coordinate matrix of the second frame of scene image.
4) And matching the blind pixel coordinate matrixes of the first frame of scene image and the second frame of scene image to determine the final blind pixel position.
2. The infrared focal plane array dynamic blind pixel detection method based on scene matching according to claim 1, characterized in that: the specific calculation method of the step 1) is as follows:
setting the central pixel response value of f frame in the sliding window as Xf(i, j), the temporal noise of the pixel is defined as σF(i,j)，Is the average value of all pixel response values in the F frame scene image, then sigmaF(i, j) is:
wherein,
the adaptive threshold value delta (i, j) corresponding to the center pixel of the sliding window is as follows:
wherein,
is the time domain noise sigma of all pixels with X (i, j) as the center in the sliding windowF(i, j) and F represents the number of frames collected.
3. The infrared focal plane array dynamic blind pixel detection method based on scene matching according to claim 1 or 2, characterized in that: the set threshold value T is an empirical value, and the value range is generally between 0.4 and 0.6.
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| CN110579283A (en) * | 2019-09-18 | 2019-12-17 | 北京理工大学 | HDR dynamic infrared radiation source array target |
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| CN111008944A (en) * | 2019-12-25 | 2020-04-14 | 武汉高德红外股份有限公司 | Method and system for searching unstable pixels of infrared focal plane detector |
| CN111008944B (en) * | 2019-12-25 | 2023-07-14 | 武汉高德红外股份有限公司 | Method and system for searching unstable pixels of infrared focal plane detector |
| CN114877998A (en) * | 2022-02-15 | 2022-08-09 | 东莞市鑫泰仪器仪表有限公司 | Infrared image blind pixel processing method and thermal infrared imager |
| CN117475242A (en) * | 2023-12-27 | 2024-01-30 | 南京信息工程大学 | Infrared detector blind pixel dynamic detection method based on progressive classification model |
| CN117475242B (en) * | 2023-12-27 | 2024-03-08 | 南京信息工程大学 | Infrared detector blind pixel dynamic detection method based on progressive classification model |
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