CN103793900B - A kind of infrared blind element compensation method returning based on mixed self-adapting - Google Patents

Abstract

The invention discloses a kind of infrared blind element compensation method returning based on mixed self-adapting, first carry out multiple dimensioned decomposition by Curvelet transfer pair infrared image I, be decomposed into I₁,…,I_n-1,I_n, n is natural number; Secondly the each high-order scale subbands imagery exploitation nonparametric model after decomposing is carried out to blind element recovery, obtain image I_1r,…,I_n-1r,I_nr; To repeatedly iteration of nonparametric model, to improve the precision of non-parametric estmation; Again utilize parameter model to being connected between each high-order yardstick and study, by parametric technique interpolation I_nrObtain image I_n-1i, and make its prior image as n-1 layer yardstick; Finally, the above step of step-by-step recursion, finally exports Recovery image. The present invention is from multiscale analysis angle, combining adaptive regression model, solve blind element has been detected to the problem too relying on, all there is good adaptability for thering are a large amount of images random or fixing blind element simultaneously, and verified by experiment validity and the practicality of new algorithm.

Description

A kind of infrared blind element compensation method returning based on mixed self-adapting

Technical field

The present invention relates to a kind of infrared blind element compensation method, particularly a kind of infrared blind element compensation returning based on mixed self-adaptingMethod.

Background technology

Along with the expansion of infrared imaging device application, people have also proposed more and more higher requirement to its image quality. But byIn the impact of the factor such as manufacturing process, material, IRFPA (Infraredfocalplanearray) device inevitably exist noise,The problems such as heterogeneity. And the problems referred to above can directly cause the generation of blind element, if in the time of imaging without corresponding processing, blind elementCan make image occur bright spot or dim spot, have a strong impact on image quality. Therefore it is crucial, in infrared imaging system, rejecting blind elementNonuniformity Correction step, blind element compensation is the key of rejecting blind element.

At present traditional infrared blind element compensation method is relatively simple and single, mainly comprises: linear interpolation method, Neighborhood Filtering method,The distortion of median filtering method and these algorithms. And the compensation effect of all kinds of backoff algorithms can mostly depend on not undetected as much as possibleWith mistake inspection blind element. Blind element is undetected can affect the inhibition to bright spot, dim spot; Blind element is crossed inspection can lose real information. Meanwhile,Existing backoff algorithm is mainly to substitute to compensate blind element by simple neighborhood territory pixel, lacks the adaptability to blind element bunch image,Easily cause fuzzy details, produce serious distortion.

Summary of the invention

Technical problem to be solved by this invention is to provide a kind of infrared blind element backoff algorithm based on mixed self-adapting regression model,From multiscale analysis angle, combining adaptive regression model, has solved blind element has been detected to the problem too relying on, simultaneously for toolThere are a large amount of images random or fixing blind element all to there is good adaptability, and verified by experiment validity and the reality of new algorithmThe property used.

The present invention is for solving the problems of the technologies described above by the following technical solutions:

The infrared blind element compensation method returning based on mixed self-adapting, comprises following concrete steps:

Step 1, by Curvelet transfer pair infrared image, I carries out multiple dimensioned decomposition, obtains the each scalogram picture after decomposingI₁,…,I_n-1,I_n, n is natural number;

Step 2, returns compensation to the each yardstick image configuration nonparametric model after decomposing, and recovers to realize blind element, obtainsRecover the image I of blind element_1r,…,I_n-1r,I_nr; Wherein, described structure nonparametric model returns compensation, is specially:

First, utilize blind element to detect and obtain blind element index matrix, learn the particular location of blind element;

Then, in the each scalogram picture from decomposing, obtain the neighborhood territory pixel collection of its blind element, carry out nonparametric by nonparametric modelEstimate, obtain the estimated value of blind element pixel, thus the image of the blind element that is restored;

Step 3, builds auto-regressive parameter model to recovering in step 2 between the image after blind element, carries out the linking between each yardstickWith study, by the image I of parametric technique demosaicing blind element_nrObtain interpolation image I_n-1i, and set it as n-1 layer yardstickPrior image; Wherein, described structure auto-regressive parameter model, is specially:

First, from the recovery blind element image of step 2, obtain known blind element set of pixels and neighborhood territory pixel collection thereof, composition linear equationAnd try to achieve total least square solution;

Then, utilize the linearity of blind element set of pixels in required total least square solution, interpolation image and neighborhood territory pixel collection composition thereofEquation, tries to achieve blind element set of pixels and the concentrated unknown pixel of neighborhood territory pixel thereof in interpolation image by least square method, therebyTo interpolation image;

Step 4, step-by-step recursion step 2-step 3, finally exports Recovery image.

As further prioritization scheme of the present invention, in step 2 to repeatedly iteration of nonparametric model, to improve non-parametric estmationPrecision; Described iterations is three times.

The present invention adopts above technical scheme compared with prior art, has overcome existing algorithm and has too relied on blind element detection and self adaptationThe deficiency of ability, looks for another way, and from multiscale analysis angle combining adaptive regression model, has solved blind element was detectedIn the problem relying on, all there is good adaptability for thering are a large amount of images random or fixing blind element simultaneously, and by experimentValidity and the practicality of new algorithm are verified. Blind element compensation strong adaptability of the present invention, for having a large amount of isolated and blind elements bunchImage all can effective compensation.

Brief description of the drawings

Fig. 1 is flow chart of the present invention.

Fig. 2 is the flow chart of the embodiment of the present invention.

Detailed description of the invention

Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

The infrared blind element compensation method returning based on mixed self-adapting, as shown in Figure 1, comprises following concrete steps:

Step 1, by Curvelet transfer pair infrared image, I carries out multiple dimensioned decomposition, obtains the each scalogram picture after decomposingI₁,…,I_n-1,I_n, n is natural number;

Step 2, returns compensation to the each yardstick image configuration nonparametric model after decomposing, and recovers to realize blind element, obtainsRecover the image I of blind element_1r,…,I_n-1r,I_nr; Wherein, described structure nonparametric model returns compensation, is specially:

First, utilize blind element to detect and obtain blind element index matrix, learn the particular location of blind element;

Then, in the each scalogram picture from decomposing, obtain the neighborhood territory pixel collection of its blind element, carry out nonparametric by nonparametric modelEstimate, obtain the estimated value of blind element pixel, thus the image of the blind element that is restored;

Step 3, builds auto-regressive parameter model to recovering in step 2 between the image after blind element, carries out the linking between each yardstickWith study, by the image I of parametric technique demosaicing blind element_nrObtain interpolation image I_n-1i, and set it as n-1 layer yardstickPrior image; Wherein, described structure auto-regressive parameter model, is specially:

First, from the recovery blind element image of step 2, obtain known blind element set of pixels and neighborhood territory pixel collection thereof, composition linear equationAnd try to achieve total least square solution;

Then, utilize the linearity of blind element set of pixels in required total least square solution, interpolation image and neighborhood territory pixel collection composition thereofEquation, tries to achieve blind element set of pixels and the concentrated unknown pixel of neighborhood territory pixel thereof in interpolation image by least square method, therebyTo interpolation image;

Step 4, step-by-step recursion step 2-step 3, finally exports Recovery image.

By following specific embodiment, method of the present invention is further elaborated, as shown in Figure 2.

1, input blind element imageBy imageConvert respectively and form by CurveletThe embodiment decomposition that haves three layers altogether,Be specially: first willBe decomposed into low frequency sub-bandAnd high-frequency sub-bandThen high-frequency sub-band is decomposed into 2 through direction bank of filters againIndividual directional subband, wherein low frequency sub-band is

2, estimate by nonparametric modelMiddle blind element pixel value, obtains low-resolution imageSpecific as follows:

1) utilize blind element to detect the blind element index matrix obtaining, the particular location of known blind element; Use y₁,…,y_nRepresent that n the unknown is blindUnit, n is natural number, y₀For the general name of unknown blind element;

2) fromIn obtain its set of pixels { y₁,…,y_nAnd { x₀,x₁,…,x_n}；x₀,x₁,…,x_nFor blind element y₀Neighborhood territory pixel;

If may there is blind element pixel in neighborhood in compensation for the first time, remove the impact of blind element by mask vector, mask toIn amount, " 1 " represents normal pixel, and " 0 " represents blind element, the following formula of substitution:

${\hat{y}}_0=\frac{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{K}_h\left(\right|\left|m^T\right(x_0-x_j\left)\right|\left|\right)y_j}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{K}_h\left(\right|\left|m^T\right(x_0-x_j\left)\right|\left|\right)}$

In formula,For blind element y₀Pixel estimated value; M is mask vector; K_hFor the standard deviation gaussian kernel function that is 4;

If not compensation for the first time, the direct following formula of substitution:

${\hat{y}}_0\≈\frac{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{K}_h\left(\right||x_0-x_j|\left|\right)y_j}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{K}_h\left(\right||x_0-x_j|\left|\right)}$

3) by continuous iterationCan carry out non-parametric estmation repeatedly; The present embodiment iterations is 3 times;

3, by parameter model interpolationObtain high-definition pictureSpecific as follows:

1) according to fromIn the known pixels collection { y that obtains₁,…,y_nAnd { x₀,x₁,…,x_nForm X and Y, linear equation composed as follows,And adopt total least square method to solve to draw total least square solution

$Y=X{\hat{a}}_{TLS}$

2) high-definition pictureSet of pixelsWithComprise known low-resolution pixel and unknown highResolution ratio pixel, equally this two groups of pixel set and above-mentioned trying to achieveCan form equation

3) solve and draw by least square methodWithThe high-resolution pixel value of middle the unknown, therebyArrive

4, utilize imageAs prior estimate, estimate to obtain by nonparametric modelIts detailed process is with step 2, only notCross and collecting { x₀,x₁,…,x_nTime utilize

5, by parameter model interpolationObtainIts detailed process is with step 3;

6, utilize imageAs prior estimate, estimate to obtain by nonparametric modelIts detailed process is with step 4, finalObtain exporting Recovery image

The above, be only the detailed description of the invention in the present invention, but protection scope of the present invention is not limited to this, any ripeKnow the people of this technology in the disclosed technical scope of the present invention, can understand conversion or the replacement expected, all should be encompassed in the present inventionComprise scope within, therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.

Claims (4)

1. the infrared blind element compensation method returning based on mixed self-adapting, is characterized in that, comprises following concrete steps:

Step 1, by Curvelet transfer pair infrared image, I carries out multiple dimensioned decomposition, obtains the each scalogram picture after decomposingI₁,…,I_n-1,I_n, n is natural number;

Step 2, returns compensation to the each yardstick image configuration nonparametric model after decomposing, and recovers to realize blind element, obtainsRecover the image I of blind element_1r,…,I_n-1r,I_nr; Wherein, described structure nonparametric model returns compensation, is specially:

First, utilize blind element to detect and obtain blind element index matrix, learn the particular location of blind element;

Then, in the each scalogram picture from decomposing, obtain the neighborhood territory pixel collection of its blind element, carry out nonparametric by nonparametric modelEstimate, obtain the estimated value of blind element pixel, thus the image of the blind element that is restored;

Step 3, builds auto-regressive parameter model to recovering in step 2 between the image after blind element, carries out the linking between each yardstickWith study, by the image I of parametric technique demosaicing blind element_nrObtain interpolation image I_n-1i, and set it as n-1 layer yardstickPrior image; Wherein, described structure auto-regressive parameter model, is specially:

First, from the recovery blind element image of step 2, obtain known blind element set of pixels and neighborhood territory pixel collection thereof, composition linear equationAnd try to achieve total least square solution;

Then, utilize the linearity of blind element set of pixels in required total least square solution, interpolation image and neighborhood territory pixel collection composition thereofEquation, tries to achieve blind element set of pixels and the concentrated unknown pixel of neighborhood territory pixel thereof in interpolation image by least square method, therebyTo interpolation image;

Step 4, step-by-step recursion step 2-step 3, finally exports Recovery image.

2. a kind of infrared blind element compensation method returning based on mixed self-adapting according to claim 1, is characterized in that:In step 2 to repeatedly iteration of nonparametric model, to improve the precision of non-parametric estmation.

3. a kind of infrared blind element compensation method returning based on mixed self-adapting according to claim 2, is characterized in that:Described to nonparametric model repeatedly the number of times of iteration be three times.

4. a kind of infrared blind element compensation method returning based on mixed self-adapting according to claim 1, is characterized in that:When step 1 is carried out multiple dimensioned decomposition, n is larger, and yardstick is higher, and resolution ratio is lower.