CN105741238B - A kind of Infrared Image Non-uniformity Correction method based on scene interframe registration - Google Patents
A kind of Infrared Image Non-uniformity Correction method based on scene interframe registration Download PDFInfo
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Abstract
The invention discloses a kind of Infrared Image Non-uniformity Correction methods based on scene interframe registration, by by the collected raw analog image of infrared detector after A/D is converted, obtain original digital image, then original digital image is corrected, the output pixel matrix Y of the (n-1)th frame after being correctedn‑1The output pixel matrix Y of n-th frame after (i, j) and correctionn(i, j), the output pixel matrix Y of the (n-1)th frame after statistical correctionn‑1(i, j), and it is averaged averagen.Then pass through the output pixel matrix Y to the (n-1)th frame after correctionn‑1(i, j) self-adjusting, threshold value ERRn(i, j) self-adjusting is completed with convergence step-length step self-adjusting to on+1(i, j) and gn+1(i, j) is updated, to complete to carry out entire image the process of Nonuniformity Correction.The present invention has the characteristics that simple, efficient, and rate of convergence is fast, can correct non-homogeneous stronger image, and have good calibration result in face of different scenes, applicability is wide.
Description
Technical field
The technology of the present invention belongs to Infrared Image Non-uniformity Correction field, and in particular to it is a kind of based on scene interframe registration
Infrared Image Non-uniformity Correction method.
Background technique
The visual field that infra-red radiation recognizes that the mankind for the Nature is opened up, external red in infrared imaging system
External radiation is passed to infrared detector by optical system, focuses in thermal element, infrared energy is converted into electricity by detector
Signal, the power of electric signal In situ FTIR radiation energy, after amplification, AD sampling and signal processing, in display system
The upper infrared image for forming observable.
Infrared imaging system is imaged using the difference of body surface temperature.The response of front-end detector is not absolute
Linear, but generally enhance with the enhancing of incident radiation, it is imaged on a monitor using the signal processing of rear end.By
In using infra-red radiation be formed by image only it is related with target surface temperature, it is unrelated with color of object, so on a monitor
Imaging is the different gray level image of brightness, is not color image.
Ideally, the response curve of each image-sensitive pixel should be identical on infrared focal plane detector
's.However in practical situations, the semiconductor material of infrared focal plane detector itself, technologic defect etc. can all cause red
Each image-sensitive member on outer focus planar detector has different response characteristics, i.e., under uniform infra-red radiation, detector response
Value has otherness, and heteropical weight is also related to material for detector and technique.Furthermore infrared image is showed
Heterogeneity it is related with the various pieces of entire infrared imaging system, optical system, reading circuit etc. can all influence it is non-
Even property, the influence of this part is difficult to differentiate and separate from overall heterogeneity, so from broadly, heterogeneity is
Refer to the inconsistency of each image-sensitive member response of infrared focal plane detector in the case where uniform infra-red radiation is incident.
With the development of Nonuniformity Correction, formed at present a variety of non-based on calibrating and being based on scene two major classes
Homogeneity correction algorithm.But without a kind of algorithm can in most cases can well-corrected, only can be in certain spies
Obtain good calibration result under conditions of fixed, it is on the contrary then cannot correct, or even the quality of the original image of destruction.Class is calibrated to calculate
Method complexity is lower, calculation amount is smaller, is easy to engineer application, but always need manual operation, and adaptive response is lower, certain to demarcate
Correction parameter can not be just changed afterwards.Algorithm based on scene class but just in contrast, Non-uniformity Correction Algorithm be able to achieve from
Correction parameter is adapted to, is participated in without artificial.Algorithm based on scene class is divided into constant constant statistics method, neural network algorithm and frame
Between method for registering etc..However neural network nonuniformity correcting algorithm is difficult the receipts with higher while guaranteeing calibration result
Speed is held back, it, cannot be very when situation stronger in face of heterogeneity because the neural network structure that it is established is open loop
Good correction, or even the case where will appear diverging, when image freeze, image just will appear serious decay；Constant is permanent
Determine the use premise of statistic law, once image freeze, that is, be not present randomness or randomness is weaker, which cannot work,
More serious ghost will be left on the image when scene moves again, enable picture quality slump of disastrous proportions；Interframe registration
When situation stronger in face of heterogeneity, block-like heterogeneity especially is formed in image surrounding, cannot be corrected well,
And rate of convergence is slow, cannot obtain good picture quality.
Summary of the invention
The purpose of the present invention is to provide a kind of Infrared Image Non-uniformity Correction method based on scene interframe registration, solutions
Slump of disastrous proportions phenomenon, rate of convergence occurs in image when image freeze of having determined cannot obtain well slowly, when in face of non-homogeneous stronger
The problem of picture quality.
The technical solution for realizing the aim of the invention is as follows: a kind of infrared image heterogeneity based on scene interframe registration
Bearing calibration, method and step are as follows:
Step 1, by the collected raw analog image of infrared detector after A/D is converted, obtain original digital image.
Original digital image is corrected by step 2, the output pixel matrix Y of the (n-1)th frame after being correctedn-1(i, j)
With the output pixel matrix T of n-th frame after correctionn(i, j), updating formula are as follows:
Wherein, (i, j) indicates the coordinate value of pixel, Xn(i, j) is the input picture element matrix of original image n-th frame, gn(i,
J) the correcting gain parameter of n-th frame, o are indicatedn(i, j) indicates the correction offset parameter of n-th frame, ERRn(i, j) is the threshold of n-th frame
Value, step are convergence step-length, dxIndicate offset of the n-th frame image relative to the (n-1)th frame image on the direction reference axis x, dyTable
Show offset of the n-th frame image relative to the (n-1)th frame image on the direction reference axis y.
Step 3, the output pixel matrix Y for determining the (n-1)th frame after above-mentioned correctionn-1The average value of (i, j): after statistical correction
The output pixel matrix Y of (n-1)th framen-1(i, j), and it is averaged averagen, n expression frame number.
Step 4, to the (n-1)th frame output pixel matrix Y after correctionn-1The pixel value y of each pixel of (i, j)n-1Respectively
It is adjusted, the picture element matrix Y ' after being adjustedn-1(i, j), and by picture element matrix Y ' adjustedn-1(i, j) substitutes into step 2
Formula 2. in, obtain the threshold value ERR ' of updated n-th framen(i, j), adjustment formula are as follows:
Wherein, p1And p2Indicate the adjusted value of corresponding pixel points, range is respectively as follows: p1∈ [0,50], p2∈ [50,
100]；Q indicates the adjusted value of average pixel value, in the range of q ∈ [0,200]；n1It indicates to stop frame number, n1Range is n1∈
[100,150].
Step 5, to threshold value ERR ' updated in step 4nThe self-adjusting of (i, j) row:
To updated threshold value ERR 'n(i, j) is adjusted, the threshold value ERR " after being adjustedn(i, j), formula are as follows:
Pa indicates the limits value of inhibition ghost phenomenon, and range is pa ∈ [100,300]；n2It indicates to distinguish frame number, range are as follows:
n2∈ [10,20].
Step 6, convergence step-length step carry out self-adjusting:
Self-adjusting is carried out to convergence step-length step, formula is as follows:
step1And step2Respectively indicate convergence step constant, step1Greater than step2, range is respectively as follows: step1∈
[0.08,0.1], step2∈ [0.01,0.03].
Step 7, by threshold value ERR " adjustedn(i, j) and convergence step-length step adjusted substitute into the formula of step 2
3. with formula 4. in, respectively to on+1(i, j) and gn+1(i, j) is updated, and obtains updated o 'n+1(i, j) and g 'n+1(i,
j)。
Step 8, by updated o 'n+1(i, j) and g 'n+11. (i, j) substitutes into the formula of step 2, after being corrected
The output pixel matrix Y ' of (n+1)th framen+1(i, j).
Above-mentioned steps 5 and step 6 sequence are exchanged.
Compared with prior art, the present invention its remarkable advantage: (1) rate of convergence is fast, and the present invention can be in the very short time
It is interior that Nonuniformity Correction is carried out to image, save the time；(2) calibration result is obvious, serious red in face of surrounding heterogeneity
Outer image can carry out good correction to image, obtain preferable picture quality；(3) practicability is wide, in face of different scenes
There is good calibration result.
Detailed description of the invention
Fig. 1 is a kind of method flow of the Infrared Image Non-uniformity Correction method based on scene interframe registration of the present invention
Figure.
Fig. 2 is that night high-altitude of the present invention by the Infrared Image Non-uniformity Correction method being registrated based on scene interframe is clapped
It photographs the effect at the mountain ridge and compares figure；Wherein (a) is the infrared original image under night high-altitude is shot at the mountain ridge, (b) is school
Image after just.
Fig. 3 is that night high-altitude of the present invention by the Infrared Image Non-uniformity Correction method being registrated based on scene interframe is clapped
The effect for the viaduct photographed compares figure；Wherein (a) is the infrared original image of viaduct under the shooting of night high-altitude, (b) is school
Image after just.
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawing.
In conjunction with Fig. 1, a kind of Infrared Image Non-uniformity Correction method based on scene interframe registration, method and step is as follows:
Step 1, by the collected raw analog image of infrared detector after A/D is converted, obtain original digital image；
Original digital image is corrected by step 2, the output pixel matrix Y of the (n-1)th frame after being correctedn-1(i, j)
With the output pixel matrix Y of n-th frame after correctionn(i, j), updating formula are as follows:
Wherein, (i, j) indicates the coordinate value of pixel, Xn(i, j) is the input picture element matrix of original image n-th frame, gn(i,
J) the correcting gain parameter of n-th frame, o are indicatedn(i, j) indicates the correction offset parameter of n-th frame, ERRn(i, j) is the threshold of n-th frame
Value, step are convergence step-length, dxIndicate offset of the n-th frame image relative to the (n-1)th frame image on the direction reference axis x, dyTable
Show offset of the n-th frame image relative to the (n-1)th frame image on the direction reference axis y；
Step 3, the output pixel matrix Y for determining the (n-1)th frame after above-mentioned correctionn-1The average value of (i, j): after statistical correction
The output pixel matrix Y of (n-1)th framen-1(i, j), and it is averaged averagen, n expression frame number；
Step 4, to the (n-1)th frame output pixel matrix Y after correctionn-1The pixel value y of each pixel of (i, j)n-1Respectively
It is adjusted, the picture element matrix Y ' after being adjustedn-1(i, j), and by picture element matrix Y ' adjustedn-1(i, j) substitutes into step 2
Formula 2. in, obtain the threshold value ERR ' of updated n-th framen(i, j), adjustment formula are as follows:
Wherein, p1And p2Indicate the adjusted value of corresponding pixel points, range is respectively as follows: p1∈ [0,50], p2∈ [50,
100]；Q indicates the adjusted value of average pixel value, in the range of q ∈ [0,200]；n1It indicates to stop frame number, n1Range is n1∈
[100,150]；p1And p2When being 0, indicate that adjustment terminates；
Step 5, to threshold value ERR ' updated in step 4nThe self-adjusting of (i, j) row:
To updated threshold value ERR 'n(i, j) is adjusted, the threshold value ERR " after being adjustedn(i, j), formula are as follows:
Pa indicates the limits value of inhibition ghost phenomenon, and range is pa ∈ [100,300]；n2It indicates to distinguish frame number, range are as follows:
n2∈ [10,20]；
Step 6, convergence step-length step carry out self-adjusting:
Self-adjusting is carried out to convergence step-length step, formula is as follows:
step1And step2Respectively indicate convergence step constant, step1Greater than step2, range is respectively as follows: step1∈
[0.08,0.1], step2∈ [0.01,0.03]；
Step 7, by threshold value ERR " adjustedn(i, j) and convergence step-length step adjusted substitute into the formula of step 2
3. with formula 4. in, respectively to on+1(i, j) and gn+1(i, j) is updated, and obtains updated o 'n+1(i, j) and g 'n+1(i,
j)；
Step 8, by updated o 'n+1(i, j) and g 'n+11. (i, j) substitutes into the formula of step 2, after being corrected
The output pixel matrix Y ' of (n+1)th framen+1(i, j).
Embodiment 1
In conjunction with Fig. 2, infrared detector collects the infrared image under night high-altitude is shot at the mountain ridge, and image size is 320
×256；
Step 1, by the collected raw analog image of infrared detector after A/D is converted, obtain original digital image；
Original digital image is corrected by step 2, the output pixel matrix Y of the (n-1)th frame after being correctedn-1(i, j)
With the output pixel matrix Y of n-th frame after correctionn(i, j), updating formula are as follows:
Step 3, the output pixel matrix Y for determining the (n-1)th frame after above-mentioned correctionn-1The average value of (i, j): after statistical correction
The output pixel matrix Y of (n-1)th framen-1(i, j), and it is averaged averagen, n expression frame number；
Step 4, to the (n-1)th frame output pixel matrix Y after correctionn-1The pixel value y of each pixel of (i, j)n-1Respectively
It is adjusted, the picture element matrix Y ' after being adjustedn-1(i, j), and by picture element matrix Y ' adjustedn-1(i, j) substitutes into step 2
Formula 2. in, obtain the threshold value ERR ' of updated n-th framen(i, j), adjustment formula are as follows:
Wherein, p1And p2Indicate the adjusted value of corresponding pixel points, range is respectively as follows: p1∈ [0,50], p2∈ [50,
100]；Q indicates the adjusted value of average pixel value, in the range of q ∈ [0,200]；n1It indicates to stop frame number, n1Range is n1∈
[100,150]；
As n-1 >=n1When, p1And p2It is 0, then by yn-1(i, j) directly charges to Y 'n-1(i, j).
According to Y 'n-1(i, j) and Yn(i, j) obtains the threshold value ERR ' of updated n-th framen(i, j).
Step 5, to threshold value ERR ' updated in step 4nThe self-adjusting of (i, j) row:
To updated threshold value ERR 'n(i, j) is adjusted, the threshold value ERR " after being adjustedn(i, j), formula are as follows:
Pa indicates the limits value of inhibition ghost phenomenon, and range is pa ∈ [100,300]；n2It indicates to distinguish frame number, range are as follows:
n2∈ [10,20]；
Step 6, convergence step-length step carry out self-adjusting:
Self-adjusting is carried out to convergence step-length step, formula is as follows:
step1And step2Respectively indicate convergence step constant, step1Greater than step2, range is respectively as follows: step1∈
[0.08,0.1], step2∈ [0.01,0.03]；
Step 7, by threshold value ERR " adjustedn(i, j) and convergence step-length step adjusted substitute into the formula of step 2
3. with formula 4. in, respectively to on+1(i, j) and gn+1(i, j) is updated, and obtains updated o 'n+1(i, j) and g 'n+1(i,
j)。
Step 8, by updated o 'n+1(i, j) and g 'n+11. (i, j) substitutes into the formula of step 2, after being corrected
The output pixel matrix Y ' of (n+1)th framen+1(i, j).
Comparison diagram 2 (a) and (b), the non-homogeneous stronger region of image surrounding is corrected from calibration result, and reduction is true
Image, and rate of convergence is fast, is corrected in very short frame number to image, finally obtains a second best in quality figure
Picture.
Embodiment 2
In conjunction with Fig. 3, infrared detector collects the viaduct infrared image under the shooting of night high-altitude, and image size is 320
×256；
Step 1, by the collected raw analog image of infrared detector after A/D is converted, obtain original digital image；
Original digital image is corrected by step 2, the output pixel matrix Y of the (n-1)th frame after being correctedn-1(i, j)
With the output pixel matrix Y of n-th frame after correctionn(i, j), updating formula are as follows:
Step 3, the output pixel matrix Y for determining the (n-1)th frame after above-mentioned correctionn-1The average value of (i, j): after statistical correction
The output pixel matrix Y of (n-1)th framen-1(i, j), and it is averaged averagen, n expression frame number；
Step 4, to the (n-1)th frame output pixel matrix Y after correctionn-1The pixel value y of each pixel of (i, j)n-1Respectively
It is adjusted, the picture element matrix Y ' after being adjustedn-1(i, j), and by picture element matrix Y ' adjustedn-1(i, j) substitutes into step 2
Formula 2. in, obtain the threshold value ERR ' of updated n-th framen(i, j), adjustment formula are as follows:
Wherein, p1And p2Indicate the adjusted value of corresponding pixel points, range is respectively as follows: p1∈ [0,50], p2∈ [50,
100]；Q indicates the adjusted value of average pixel value, in the range of q ∈ [0,200]；n1It indicates to stop frame number, n1Range is n1∈
[100,150]；
As n-1 >=n1When, p1And p2It is 0, then by yn-1(i, j) directly charges to Y 'n-1(i, j).
According to Y 'n-1(i, j) and Yn(i, j) obtains the threshold value ERR ' of updated n-th framen(i, j).
Step 5, convergence step-length step carry out self-adjusting:
Self-adjusting is carried out to convergence step-length step, formula is as follows:
step1And step2Respectively indicate convergence step constant, step1Greater than step2, range is respectively as follows: step1∈
[0.08,0.1], step2∈ [0.01,0.03]；
Step 6, to threshold value ERR ' updated in step 4nThe self-adjusting of (i, j) row:
To updated threshold value ERR 'n(i, j) is adjusted, the threshold value ERR " after being adjustedn(i, j), formula are as follows:
Pa indicates the limits value of inhibition ghost phenomenon, and range is pa ∈ [100,300]；n2It indicates to distinguish frame number, range are as follows:
n2∈ [10,20]；
Step 7, by threshold value ERR " adjustedn(i, j) and convergence step-length step adjusted substitute into the formula of step 2
3. with formula 4. in, respectively to on+1(i, j) and gn+1(i, j) is updated, and obtains updated o 'n+1(i, j) and g 'n+1(i,
j)。
Step 8, by updated o 'n+1(i, j) and g 'n+11. (i, j) substitutes into the formula of step 2, after being corrected
The output pixel matrix Y ' of (n+1)th framen+1(i, j).
Comparison diagram 3 (a) and (b), the non-homogeneous stronger region of image surrounding is corrected from calibration result, and reduction is true
Image, and rate of convergence is fast, is corrected in very short frame number to image, finally obtains a second best in quality figure
Picture.
Claims (2)
1. a kind of Infrared Image Non-uniformity Correction method based on scene interframe registration, which is characterized in that method and step is as follows:
Step 1, by the collected raw analog image of infrared detector after A/D is converted, obtain original digital image；
Original digital image is corrected by step 2, the output pixel matrix Y of the (n-1)th frame after being correctedn-1(i, j) and school
The output pixel matrix Y of n-th frame after justn(i, j), updating formula are as follows:
Wherein, (i, j) indicates the coordinate value of pixel, Xn(i, j) is the input picture element matrix of original image n-th frame, gn(i, j) table
Show the correcting gain parameter of n-th frame, on(i, j) indicates the correction offset parameter of n-th frame, ERRn(i, j) is the threshold value of n-th frame,
Step is convergence step-length, dxIndicate offset of the n-th frame image relative to the (n-1)th frame image on the direction reference axis x, dyIt indicates
Offset of the n-th frame image relative to the (n-1)th frame image on the direction reference axis y；
Step 3, the output pixel matrix Y for determining the (n-1)th frame after above-mentioned correctionn-1The average value of (i, j): (n-1)th after statistical correction
The output pixel matrix Y of framen-1(i, j), and it is averaged averagen, n expression frame number；
Step 4, to the (n-1)th frame output pixel matrix Y after correctionn-1The pixel value Y of each pixel of (i, j)n-1(i, j) difference
It is adjusted, the picture element matrix Y ' after being adjustedn-1(i, j), and by picture element matrix Y ' adjustedn-1(i, j) substitutes into step 2
Formula 2. in, obtain the threshold value ERR ' of updated n-th framen(i, j), adjustment formula are as follows:
Wherein, p1And p2Indicate the adjusted value of corresponding pixel points, range is respectively as follows: p1∈ [0,50], p2∈ [50,100]；q
The adjusted value for indicating average pixel value, in the range of q ∈ [0,200]；n1It indicates to stop frame number, n1Range is n1∈ [100,
150]；
Step 5, to threshold value ERR ' updated in step 4n(i, j) carries out self-adjusting:
To updated threshold value ERR 'n(i, j) is adjusted, the threshold value ERR " after being adjustedn(i, j), formula are as follows:
Pa indicates the limits value of inhibition ghost phenomenon, and range is pa ∈ [100,300]；n2It indicates to distinguish frame number, range are as follows: n2∈
[10,20]；
Step 6, convergence step-length step carry out self-adjusting:
Self-adjusting is carried out to convergence step-length step, formula is as follows:
step1And step2Respectively indicate convergence step constant, step1Greater than step2, range is respectively as follows: step1∈ [0.08,
0.1], step2∈ [0.01,0.03]；
Step 7, by threshold value ERR " adjustedn(i, j) and convergence step-length step adjusted substitute into the formula of step 2 3. and
Formula 4. in, respectively to on+1(i, j) and gn+1(i, j) is updated, and obtains updated o 'n+1(i, j) and g 'n+1(i, j)；
Step 8, by updated o 'n+1(i, j) and g 'n+11. (i, j) substitutes into the formula of step 2, and (n+1)th after being corrected
The output pixel matrix Y ' of framen+1(i, j).
2. the Infrared Image Non-uniformity Correction method according to claim 1 based on scene interframe registration, feature exist
In: above-mentioned steps 5 and step 6 sequence are exchanged.
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