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CN105184740A - Non-uniform stripe correction method of infrared focal plane image - Google Patents

Non-uniform stripe correction method of infrared focal plane image Download PDF

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CN105184740A
CN105184740A CN 201510261452 CN201510261452A CN105184740A CN 105184740 A CN105184740 A CN 105184740A CN 201510261452 CN201510261452 CN 201510261452 CN 201510261452 A CN201510261452 A CN 201510261452A CN 105184740 A CN105184740 A CN 105184740A
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stripe
image
method
plane
focal
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CN 201510261452
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颜露新
��昌毅
许杰
罗春桉
陈立群
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华中科技大学
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Abstract

The invention provides a non-uniform stripe correction method of an infrared focal plane image. The method comprises that 1) positions of pixel points of the infrared focal plane image with stripe noise are collected; 2) unidirectional total-variation de-striping energy functional model is established for the infrared focal plane image with stripe noise; 3) an auxiliary variable is introduced to replace a non-differentiable item in the energy functional model to obtain a new energy function; and 4) a problem to be solved is divided into three sub problems according to the new energy function, the three sub problems are solved in an alternative iteration manner, and a corrected image f is output. The method utilizes the characteristic that the stripe influences the gradient vertical to the stripe but not other gradients, the unidirectional stripe noise can be rapidly and effectively removed, advantages of stripe removing and image detail storing are combined, the adaptability is high, and the computation complexity is low.

Description

一种红外焦平面图像非均匀性条带校正方法 An infrared focal plane image nonuniformity correction method of the strip

技术领域 FIELD

[0001] 本发明属于红外图像处理领域,更具体地,涉及到一种红外焦平面图像非均匀性条带校正方法,适用于快速去除多传感元成像数据中的单方向条带噪声。 [0001] The present invention belongs to the field of infrared image processing, and more particularly, relates to an image non-uniformity of infrared focal plane strips correction method for fast removal of a single direction sensor element multiline imaging data with noise.

背景技术 Background technique

[0002] 由于红外传感器材料、工艺水平限制和外界环境的影响,红外焦平面阵列中各探测元间响应不一致,导致图像数据中出现非均匀性条带噪声,严重影响了后续的数据处理。 [0002] Since the infrared sensor material, affect the level of technology and the limitations of the external environment, IRFPA inconsistencies between each probe response element, resulting in non-uniform stripe noise appearing in the image data, a serious impact on the subsequent data processing. 条带噪声校正的困难在于有效地去除各种不同类型的条带噪声的同时较完整地保存图像原有的结构信息。 Difficulty noise correction tape that simultaneously effectively remove various types of noise bands more complete preservation of the original image structure information. 条带校正算法可以大致分为三大类: Correction algorithm can strip roughly divided into three categories:

[0003] 第一大类基于统计匹配的方法,以直方图匹配和矩匹配方法为代表。 [0003] The first category is based on statistical matching, matching and histogram matching method as a representative moments. 统计匹配的方法简单快速,易于实现。 Statistical matching simple and fast and easy to implement. 缺点是需要假设图像的行或列的统计特性一致,并且还需事先已知条带的位置,不符合实际情况,去条带效果差。 Disadvantage is the need hypotheses consistent with the statistical characteristics of the row or column of the image, and the position of the strip needs to be known in advance, not realistic, to strip poor.

[0004] 第二大类基于图像滤波技术,以低通滤波、功率滤波器为代表,优点在于只对条带进行滤波处理,而不处理非条带内容,简单易于实现。 [0004] The second category based image filtering technique, a low-pass filter, represented by the power filter, is advantageous in that only the filter strip process without processing the contents of the non-strips, simple to implement. 但,严重依赖条带检测的准确性,漏检的条带被忽略不被处理,错检则会将图像内容也滤除掉,导致信息丢失。 However, heavily dependent on the accuracy of detection of the strip, the strip is ignored missed not processed, error detection image content will be filtered out, resulting in loss of information. 实际情况中很难准确检测条带。 The actual situation is difficult to accurately detect the strip.

[0005] 第三大类基于变分正则化方法,将条带校正问题看作估计不含条带图像的逆问题,建立包含数据项和正则化项的能量函数,通过最小化能量函数迭代求解得到不含条带图像。 [0005] The third category variation regularization method based on the estimation problem as the correction tape inverse problem-free strip images, to establish the energy function of the data item and the regularization term, by minimizing the energy function iterative solver strip containing no images. Bouali 等人的文章《Toward optimal destriping of MODIS data using a unidirectional variational model》提出了一种遥感卫星图像条带校正的变分方法,其能量泛函只包含X方向的梯度域数据保真项TVx(f)和y方向上的数据约束项CiTVy (fg) 的总变分能量泛函: Bouali et al., "Toward optimal destriping of MODIS data using a unidirectional variational model" variational method proposed correction tape strip remote sensing satellite images, which includes only a gradient energy functional domain data fidelity term X direction TVx ( data on f) and y directions constraint term CiTVy (fg) of the total variation energy functional:

Figure CN105184740AD00041

[0007] 其中TV,TV ¥分别表示对图像X与y方向求总变分。 [0007] wherein TV, TV ¥ seek total variation represent X and y image directions. 第二项表示我们寻找一个和条带图像在竖直方向有几乎一样变化的图像,因为条带噪声几乎只对水平方向的图像梯度产生影响,而第一项表示只对条带图像在水平方向进行平滑,该模型有效的反映了条带的单方向性质,但是该模型结果图像与原始图像在灰度值上整体出现较大偏差。 We are looking for a second term and the slice image with an image almost identical change in the vertical direction, because the influence of noise strip almost exclusively on the image gradient in the horizontal direction, and the first term in the horizontal direction only image strips smoothing, the model reflects the effective properties of the strip in one direction, but the result of the model image and the original image on a larger tone value deviations entirety.

发明内容 SUMMARY

[0008] 本发明的目的在于提出一种红外焦平面图像非均匀性条带校正方法,将条带校正问题表示为从条带图像中估计真实图像的逆问题,利用了条带只会对垂直于其的梯度产生影响,而不影响沿其方向的梯度这一特性知识,能快速有效地去除单方向条带噪声的方法, 具有兼顾条带去除和图像细节保存的优点,适应性强,计算复杂度低。 [0008] The object of the present invention is to provide a non-uniformity of infrared focal plane image strips correction method, the problem is expressed as the correction tape from the strip inverse problem is estimated real image of the image by using only a pair of vertical strips its impact gradient, without affecting the characteristics of the gradient along the direction of the knowledge, the method can quickly and effectively remove noise in one direction with a strip has the advantage of removing both the strip and the stored image details, adaptable, calculated low complexity.

[0009] 本发明与现有技术相比,具有如下优点: [0009] Compared with the prior art the present invention has the following advantages:

[0010] 第一,兼顾条带去除和图像细节保存。 [0010] First, both the strip and remove image detail preservation. 本发明将条带校正问题表示为从条带图像中估计真实图像的逆问题,利用了条带只会对垂直于其的梯度产生影响,而不影响沿其方向的梯度这一特性知识。 The present invention represents an estimate of the strip correction problem inverse problem with the real image of the image from the article, utilizing the strip only affect its vertical gradient, without affecting the characteristics of this knowledge along the gradient direction. 既能有效地去除条带噪声,又能较好的保存图像细节。 Can effectively remove the noise band, but also better preserved image details.

[0011] 第二,计算复杂度低。 [0011] Second, the computational complexity is low. 本发明利用分裂Bregman方法进行数值优化求解,有效地规避了不可微项。 The method of the present invention using a split Bregman numerical optimization solution, effectively avoid Nondifferentiable items. 同时采用GuasS_Sidel迭代进行求解,具有更快的收敛速度。 While using GuasS_Sidel iteration is solved with a faster convergence.

[0012] 第三,适应性强。 [0012] Third, adaptability. 本发明中提供算法参数调节接口,适应多种条带噪声。 The present invention provides an interface parameter adjustment algorithm, to adapt to a variety of tape noise.

附图说明 BRIEF DESCRIPTION

[0013] 图1为本发明方法流程图; [0013] FIG. 1 is a flowchart of the present method of the invention;

[0014] 图2为真实红外图像轻度条带去除效果,图2a为原始的噪声图像;图2b为非均匀性校正后的图像;图2c为原始噪声图像与非均匀性校正后图像差值; [0014] FIG. 2 is a real IR image strips mild removal, FIG. 2a to the original noisy image; FIG. 2b image non-uniformity correction; the difference between the original image and the noise image nonuniformity correction 2c is ;

[0015] 图3为真实红外图像中度条带去除结果,图3a为原始的噪声图像;图3b为非均匀性校正后的图像;图3c为原始噪声图像与非均匀性校正后图像差值; [0015] FIG. 3 is an infrared image of the real result of the removal strip moderate, Figure 3a is the original noisy image; FIG. 3b non-uniform image after correction; the difference between the original image and the noise image nonuniformity correction of FIG. 3c ;

[0016] 图4为真实红外图像重度条带去除效果,图4a为原始的噪声图像;图4b为非均匀性校正后的图像;图4c为原始噪声图像与非均匀性校正后图像差值。 [0016] FIG. 4 is a real IR image with severe removal section, Figure 4a is an original image noise; uniformity correction after the non-image of FIG 4b; FIG. 4c a difference image after the original image and the noise non-uniformity correction.

具体实施方式 detailed description

[0017] 为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。 [0017] To make the objectives, technical solutions and advantages of the present invention will become more apparent hereinafter in conjunction with the accompanying drawings and embodiments of the present invention will be further described in detail.

[0018] 本发明根据单方向条带噪声的特点,在提出包括灰度域数据保真项 [0018] The present invention features a single direction in accordance with the noise section, including the proposed domain data fidelity term gradation

Figure CN105184740AD00051

方向的梯度域数据保真项PTVy (f_g)和X方向上的数据约束项CiTVx (f)的能量泛函,通过分裂Bregman方法求解和离散化实现。 Data on the X-direction and the gradient field data fidelity term direction PTVy (f_g) bound term CiTVx (f) of the energy functional, and solving discrete Bregman achieved by splitting method.

[0019] 实现本发明的方法是:首先利用单方向条带噪声的性质提出合理的能量泛函模型 [0019] The method of the present invention is achieved: Firstly, the direction of a single band noise properties reasonable energy functional model

Figure CN105184740AD00052

[0021] g(x,y)为探测器输出的条带图像,f(x,y)为原始无条带图像,E(f)表示关于待估计图像f的能量泛函;能量泛函包括灰度域数据保真项 [0021] g (x, y) is a band image strip detector output, f (x, y) of the original image without stripe, E (f) denote the energy on the estimated image f to be functional; energy function comprising domain data fidelity term gradation

Figure CN105184740AD00053

y方向梯度数据保真项β TVy (fg)和X方向梯度惩罚项aTVx (f);其中TVjP TV及别表示对图像X与y方向求总变分;α,β是正则化参数,用于调节正则化强度。 y-direction gradient data fidelity term β TVy (fg) and the X direction gradient penalty term aTVx (f); and wherein TVjP TV denote seek total variation of image X and y directions; α, β is a regularization parameter for adjusting the intensity of regularization. (x、y)表示图像中像素点的位置,Ω表示图像像素坐标集合。 (X, y) represents the pixel position in the image, Ω represents a set of the image pixel coordinates. 第二行为具体展开表达式。 The second expansion behavior specific expression.

[0022] 分裂Bregman方法求解引入辅助变量dx, dy, bx, by,建立新的能量泛函: [0022] split Bregman method for solving the introduction of auxiliary variables dx, dy, bx, by, create a new energy functional:

[0023] CN 105184740 AI兄明3/4 页 [0023] CN 105184740 AI Ming brother 3/4

Figure CN105184740AD00061

正则化参数,调节正则化约束强度。 Regularization parameter, adjusting the intensity of regularization constraints. λ p λ 2为惩罚参数,用于约束引入的辅助变量。 It is a penalty λ p λ 2 parameters, for assisting in the introduction of variable constraints.

[0025] 对单方向总变分去条带能量泛函模型进行迭代求解,输出得到校正后图像f。 [0025] The total variation in one direction to strip the energy functional model iterative solver, outputs the resulting corrected image f. 依据新的能量泛将求解问题拆解为三个子问题,对所述三个子问题进行交替迭代求解,输出得到校正后图像f;其中, According to the new energy functional to solve the problem disassembled into three sub-problems, the three sub-problems alternate iterative solver, outputs the resulting corrected image F; wherein,

[0026] 第一子问题为:固定d,b,求解f,得到 [0026] The first sub-problems: a fixed d, b, solving f, to give

Figure CN105184740AD00062

[0028] 第二子问题为:固定f,b,求解d,得到 [0028] The second sub-problems: a fixed f, b, solve d, to give

Figure CN105184740AD00063

[0031] 第三子问题为:固定f,d,求解b,得到 [0031] The third sub-problems: a fixed f, d, solving b, to give

Figure CN105184740AD00064

[0034] 为了方便硬件实现,采用离散化实现。 [0034] For convenience of hardware implementation using discrete implementation. 如图1所示,本发明迭代求解的具体流程如下: 1, the present invention is to solve the iterative process is as follows:

[0035] 1)输入含条带噪声的红外焦平面图像g,图像大小为MXN ;初始化迭代次数k = 1,图像fk g,辅助变量为MXN大小的零矩阵。 Infrared focal plane image g [0035] 1) containing the input band noise image size is MXN; initialization iteration k = 1, the image fk g, auxiliary variable size MXN matrix is ​​zero.

[0036] 2)将辅助变量4ΚΆΓ1,代入能量泛函迭代目标函数求解得到校正后图像fk;具体求解过程为: [0036] 2) the auxiliary variable 4ΚΆΓ1, substituting the energy function iterative solving the objective function to obtain the corrected image FK; solving process is specifically:

[0037] 固定b,d求解f的数值实现 [0037] fixed b, d f for numerical implementation

Figure CN105184740AD00065

[0039] 3)将校正后图像fk代入第二子问题求解d和第三子问题求解b对应的迭代目标函数得到辅助变量< [0039] 3) After substituting the corrected image fk second sub-problem-solving and the third sub-problem solving d b to give the corresponding objective function iteration auxiliary variables <

[0040] 固定b,f求解d的数值实现CN 105184740 A m "Ti 4/4 页 [0040] fixed b, f d for numerical implementation CN 105184740 A m "Ti 4/4 page

Figure CN105184740AD00071

[0043] 固定d,f求解b的数值实现 [0043] Fixed d, f b for numerical implementation

Figure CN105184740AD00072

[0046] 4)如果Il fk+1_fk Il 2/ Il fk+1 Il 2< ε或者迭代次数大于要求的迭代次数,ε为预定阈值,则输出fk+1作为最终处理图像,结束;否则,进入步骤5)。 [0046] 4) If Il fk + 1_fk Il 2 / Il fk + 1 Il 2 <iterations [epsilon] or the number of iterations is greater than the required, [epsilon] is a predetermined threshold value, the output fk + 1 as a final processed image, ending; otherwise, step 5).

[0047] 5)更新k = k+Ι,返回步骤3)。 [0047] 5) Update k = k + Ι, return to step 3).

[0048] 在本方法中引入了正则化参数α和β,在分裂Bregman优化求解过程中又引入惩罚参数λ^Ρ λ 2。 [0048] introduction of the regularization parameter α and β in the present method, Bregman split and introduced into the optimization process of solving penalty parameter λ ^ Ρ λ 2. 本发明针对不同强度的条带示例列出多组与之对应的参数,其仅仅作为示例,不作为对本发明限制。 With the present invention for different intensity strip of sample lists corresponding plurality of sets of parameters, which by way of example only, not by way of limitation of the invention. 迭代次数固定在40次,在轻条带下的α =20, β = 350, λ 1 =20,λ 2= 90 ;在中度条带下的α = 80,β = 800,λ 1= 30,λ 2= 110 ;在重度条带下的α = 200, β = 800, A1= 15, λ 2= 130。 40 times the number of iterations is fixed, in the light bar band for α = 20, β = 350, λ 1 = 20, λ 2 = 90; moderate strip band for α = 80, β = 800, λ 1 = 30 , λ 2 = 110; the severe strip Zones α = 200, β = 800, A1 = 15, λ 2 = 130.

[0049] 图2是真实红外噪声图像(轻度条带噪声)的条带去除结果。 [0049] FIG. 2 is a true infrared image noise (noise mild strips) result of the removal strip. 图2a为原始的噪声图像,含有轻度的条带噪声;图2b为非均匀性校正后的图像;图2c为原始噪声图像与非均匀性校正后图像差值(用于描述条带噪声去除情况),α = 20, β = 350, λ 1= 20, λ 2 =90。 2a is an image of the original noise, noise bands containing mild; FIG. 2b the image non-uniformity correction; FIG. 2c is a difference value of the original image and the noise image nonuniformity correction (to be described Destriping case), α = 20, β = 350, λ 1 = 20, λ 2 = 90. 这组图像清晰地展示了本发明对轻度噪声图像的条带具有良好的去除效果,即保留了图像本身的细节,又最大程度的去除了噪声。 This clearly shows a set of images of the present invention is a mild bar with excellent image noise removal effect, i.e., to retain details of the image itself, and the greatest degree of noise is removed.

[0050] 图3是真实红外噪声图像(中度条带噪声)的条带去除结果。 [0050] FIG. 3 is an infrared real image noise (noise moderate strips) result of the removal strip. 图3a为原始的噪声图像,含有中度的条带噪声;图3b为非均匀性校正后的图像;图3c为原始噪声图像与非均勾性校正后图像差值(用于描述条带噪声去除情况),α = 80,β = 800, Iambdal = 30, lambda2= 110。 Figure 3a is the original image noise, a noise band strip containing moderate; uniformity correction after the non-image of Figure 3b; Figure 3c is a difference between the original image and non-image noise correction are hook (band noise is used to describe removal cases), α = 80, β = 800, Iambdal = 30, lambda2 = 110. 这组图像清晰地展示了本发明对中度噪声图像的条带具有良好的去除效果,即保留了图像本身的细节(图像中的汉字依然很清晰),又最大程度的去除了噪声。 This clearly shows a set of images to the article of the present invention with moderately good image noise removal effect, i.e., to retain the details of the image itself (the image is still very clear characters), and the greatest degree of noise is removed.

[0051] 图4是真实红外噪声图像(重度条带噪声)的条带去除结果。 [0051] FIG. 4 is an infrared bar real image noise (heavy band noise) is removed with the result. 图4a为原始的噪声图像,含有重度的条带噪声;图4b为非均匀性校正后的图像;图4c为原始噪声图像与非均勾性校正后图像差值(用于描述条带噪声去除情况),α = 200,β = 800, Iambdal = 15, lambda2= 130。 4a is an original image noise, comprising a strip of severe noise; uniformity correction after the non-image of FIG 4b; FIG. 4c is a difference image after the original image and the non-flat noise correction hook (described for Destriping case), α = 200, β = 800, Iambdal = 15, lambda2 = 130. 这组图像清晰地展示了本发明对重度噪声图像的条带具有良好的去除效果,同时保留了图像本身的细节(树叶和建筑的边缘依然清晰),条带噪声被很好的抑制。 This set of images clearly show the article of the present invention to severe image noise removal with good, while retaining the details of the image itself (the edges of the leaves are still clear and buildings), the noise band is well suppressed.

[0052] 通过图2、图3、图4对比,本专利发明的方法对不同强度的条带噪声红外焦平面图像都有很好的条带去除效果,对细节的保存也是很好的。 [0052] FIGS. 2, FIG. 3, FIG. 4 compared with the method of the present patent invention has a good noise infrared focal plane of the image strips with different strengths to the article removal, save the details are good.

Claims (5)

1. 一种红外焦平面图像非均匀性条带校正方法,其特征在于,该方法具体为: 1) 采集含条带噪声的红外焦平面图像; 2) 对含条带噪声的红外焦平面图像建立单方向总变分去条带能量泛函模型: An image non-uniformity of infrared focal plane of the strip correction method, wherein the method is specifically: 1) collecting infrared focal plane image containing the noise band; 2) infrared focal plane image to the article containing noisy establishing a unidirectional tape to the total energy variation Functional model:
Figure CN105184740AC00021
其中,E(f)表示关于校正后图像f的能量泛函,g为含条带噪声的红外焦平面图像;能量泛函包括灰度域数据保真项 Wherein, E (f) denotes the corrected image on the energy functionals f, g is an infrared focal plane image containing bar noisy; energy functional domain data includes gradation fidelity term
Figure CN105184740AC00022
分别表示对校正后图像f的X 和y方向的求总变分;α,β是正则化参数,表示求偏导,(x、y)表示图像中像素点的位置,Ω表示图像像素坐标集合; 3) 引入辅助变量dx,dy,bx,by替换能量泛函模型中的不可微项,进而得到新的能量泛函: Represent the X-and y-direction corrected image f evaluates total variation; α, β is a regularization parameter, represents the partial derivative, (X, y) represents the position of the image pixel point, [Omega] represents an image pixel coordinates set ; 3) introducing an auxiliary variable dx, dy, bx, by replacing energy functional items Nondifferentiable letter model, and thus to obtain new energy functional:
Figure CN105184740AC00023
惩罚参数λ i、λ 2为用于约束引入的辅助变量; 4) 依据新的能量泛将求解问题拆解为三个子问题,对所述三个子问题进行交替迭代求解,输出得到校正后图像f;其中, 第一子问题为:固定d,b,求解f,得到: Penalty parameter λ i, λ 2 for introducing the auxiliary variable constraint; 4) based on new energy functional to solve the problem disassembled into three sub-problems, the three sub-problems alternately iterative solution to obtain a corrected output image f ; wherein the first sub-problems: a fixed d, b, solve F, to obtain:
Figure CN105184740AC00024
2. 根据权利要求1所述的红外焦平面图像非均匀性条带校正方法,其特征在于,所述步骤3)的具体实施方式为: 1) 初始化辅助变1 The image non-uniformity of infrared focal plane of the strip as claimed in claim 1, with a correction method, wherein said step 3) in specific embodiments is: 1) initializing an auxiliary variable 1
Figure CN105184740AC00031
为零矩阵和迭代次数k = 1 ; 2) 将辅助变量 Zero matrix and the number of iterations k = 1; 2) the auxiliary variable
Figure CN105184740AC00032
代入第一子问题求解f对应的迭代目标函数,求解得到校正后图像fk; 3) 将校正后图像fk代入第二子问题求解d和第三子问题求解b对应的迭代目标函数得到辅助变量 Substituting into the first sub-problem solving the objective function f corresponding to the iterative solving corrected image obtained after fk;. 3) the post-correction image fk substituting d and the second sub-problem solving problem solving b corresponding to the third sub-iteration variable auxiliary objective function obtained
Figure CN105184740AC00033
Figure CN105184740AC00034
达到预定迭代次数最大值,ε为预定阈值,则校正后图像产即为最终校正后图像f,结束;否则,进入步骤4); 5)更新k = k+Ι,返回步骤3)。 The number of iterations reaches a predetermined maximum value, the predetermined threshold value ε, the corrected image that is produced after the final corrected image F, the end; otherwise, to step 4); 5) Update k = k + Ι, return to step 3).
3. 根据权利要求2所述的红外焦平面图像非均匀性条带校正方法,其特征在于,所述第一子问题对应后的迭代目标函数为 The image non-uniformity of infrared focal plane of the strip as claimed in claim 2, with a correction method, wherein, after the iteration objective function corresponding to the first sub-problem is
Figure CN105184740AC00035
fk(i,j)是第k次迭代(i,j)位置处的像素值; fk (i, j) is the k-th iteration (i, j) the pixel value at the position;
Figure CN105184740AC00036
代表第k-Ι次迭代时的辅助变量; It represents the k-Ι auxiliary variables of iterations;
Figure CN105184740AC00037
分别代表第k-Ι次迭代时的Bregman变量;g代表原始图像。 Respectively, represents the k-Ι Bregman variables of iterations; G represents the original image.
4. 根据权利要求3所述的红外焦平面图像非均匀性条带校正方法,其特征在于,所述第二子问题对应后的迭代目标函数为: An infrared focal plane image according to claim 3, wherein said non-uniformity correction tape, characterized in that the objective function after the iteration of the second sub-problem corresponds to:
Figure CN105184740AC00038
其中,Vi是从9^到识f的线性算子,其中IT2表示N2维的实数维空间,类似地,K也是从到_濟#的线性算子。 Wherein, Vi ^ from 9 to identify linear operator f, wherein IT2 represents the real dimension N2-dimensional space, and similarly, K operator also from the economic to # _ linear. shrink是软阈值函数。 shrink soft threshold function.
Figure CN105184740AC00039
5. 根据权利要求4所述的红外焦平面图像非均匀性条带校正方法,其特征在于,所述第三子问题对应的迭代目标函数为: The infrared focal plane array of claim 4, wherein the image non-uniformity correction tape, characterized in that the objective function of the iteration corresponding to the third sub-problem are:
Figure CN105184740AC000310
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