共查询到19条相似文献,搜索用时 187 毫秒
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对红外焦平面阵列成像系统而言,基于场景的非均匀校正技术是处理固定图案噪声的关键技术。现有的非均匀校正算法主要被收敛速度和鬼像问题所限制。提出一种新的基于恒定统计算法的自适应场景非均匀校正技术。利用红外图像序列的时域统计信息结合提出的α修正均值滤波来估计探测器的参数,通过减少样本的渐进方差估计,完成成像系统的非均匀性校正。通过模拟和真实的非均匀性图像对算法的性能进行评价。实验结果表明,在继承恒定统计算法快速收敛的同时,图像峰值信噪比较恒定校正法及常系数α校正算法分别有44.5%和32.9%的提升,图像鬼像问题有明显改善。 相似文献
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针对目前红外搜索系统实用性较强的两点非均匀性校正存在难以实时跟踪图像非均匀的不足点,提出一种新的基于两点非均匀性校正和基于场景的实时联合校正算法。该算法利用两点校正提供基础校正系数,并充分利用红外搜索系统大数据量的特点,对实时数据量进行统计、分析,进而找出系统非均匀性随时间的漂移量,解决只采用两点校正算法带来的红外图像退化的问题。多次试验证明,采用联合非均匀校正算法的相对非均匀度由两点校正的5%降到了2%左右,并具有时间稳定性,获得了较好的校正效果。 相似文献
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小波变换的红外焦平面阵列非均匀性校正算法 总被引:1,自引:0,他引:1
红外焦平面阵列(IRFPA)的非均匀性是其应用中必须解决的技术难题之一。基于小波理论,提出了一种基于成像场景的红外焦平面非均匀性校正算法。该算法选择合适的小波函数对红外成像序列进行小波分解,而后对分解的信号计算出相应的统计量,从而得出红外焦平面非均匀性校正的偏置和增益校正系数,以此最终实现非均匀性校正。对真实红外序列图像的处理效果验证了该算法可较好地实现非均匀性校正。此外该算法对慢变化量具有较好的自适应性,可较好地抑制一般基于场景统计的非均匀性校正算法中出现的"人工虚影"的现象。 相似文献
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红外辐射测量系统的成像性能以及测量精度受到焦平面阵列非均匀性的严重影响,原始红外图像的非均匀性需通过后期图像处理算法进行校正。为进一步提高制冷型红外探测器的非均匀性校正(NUC)效果,本文提出了一种基于定标的非均匀性改进算法。该算法以单点定标和两点定标非均匀性校正方法为基础,既保留两点定标非均匀性校正方法增益校正系数的一致性优势,又结合了单点定标偏置校正系数的稳定性,使得改进算法具有更好的校正效果。为了验证该改进算法的校正效果,本文以640 pixel×512 pixel大小的制冷型中波红外探测器为研究对象,采用入瞳直径为25 mm的红外成像系统对提出的算法进行实验验证。实验结果表明:在1 ms积分时间下,单点定标方法、两点定标方法及改进算法校正后的图像非均匀性分别为1.783 3%、0.219 0%和0.148 1%;2 ms积分时间下的非均匀性分别为1.825 7%、2.247 4%和1.654 6%。改进算法整体上进一步降低了图像的非均匀性,校正效果更好、精度更高。 相似文献
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时域高通滤波非均匀性校正是一种典型的基于场景的红外焦平面阵列非均匀性校正算法,但其易产生"鬼影"现象,影响校正效果.本文在时域高通滤波校正算法的原理基础上,分析了其校正过程中"鬼影"现象产生的原因,即由于全部图像信息的叠加而导致静止场景被滤除且运动场景会在当前位置留下反转的图像,从而形成"鬼影".引入非局部均值滤波方法,提出了一种去"鬼影"的非局部均值滤波-时域高通滤波非均匀性校正方法.该方法首先采用非局部均值滤波将图像信息分离成高低频两部分(其中高频成分含有大部分噪音及非均匀性),并使用高频成分进行时域高通滤波算法中低通输出的递归运算,使得低通滤波后的图像含有较少的场景信息,从而可使校正输出图像含有较少的"鬼影"现象.采用两组真实红外序列图像进行验证,结果表明该算法不仅能获得较好的非均匀性校正效果,而且能较好地抑制时域高通滤波算法中的"鬼影"现象. 相似文献
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《光学学报》2017,(5)
长波红外探测器经常被用于机载红外预警系统中,常受严重的非均匀性噪声干扰。为了校正探测器的非均匀性,补偿辐射响应非线性,提出了一种基于梯度场景的非均匀性校正方法。给出了探测器辐射响应非均匀性的观测模型;以标准黑体和梯度场景作为参考源,在理论上推导出校正系数表达式;利用原理样机进行了外场实验,并探测民航客机目标。实验结果表明:与基于黑体的两点校正方法相比,利用本文方法进行非均匀性校正后的图像,局部标准差峰值由8.57降低到2.39;对于相距50.64km的空中客车A319型客机,目标的信杂比由4.87提高到11.22。本文算法可以有效降低图像局部标准差,适用于机载红外预警系统。 相似文献
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红外探测器响应漂移特性会降低红外焦平面阵列(IRFPA)非均匀性校正的精度。针对该问题提出了一种基于场景的IRFPA非均匀性校正算法。该算法利用所获得的序列成像场景信息,采用一种基于快速自适应滤波器的最优化递归估计方法来获得非均匀性校正参数,并利用当前的成像信息来更新校正参数,以此降低探测器响应漂移特性对非均匀性校正的影响。算法仿真实验显示,对非线性参数为26.12%的同一图像,使用该算法、两点校正算法和卡尔曼滤波校正算法校正1 h后,可分别将非线性参数降至1.856%,3.122%和1.893%,说明该算法可获得稳定而较好的非均匀性校正效果。 相似文献
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一种具有自适应非均匀校正功能的非制冷焦平面探测器组件 总被引:3,自引:1,他引:2
基于场景的非均匀校正算法(scene based nonuniformity correction,SBNUC)是非均匀校正技术今后的重点发展方向,介绍了近年来基于恒定统计约束的SBNUC、神经网络的SBNUC和运动估计的SBNUC算法的研究进展。研究了SBNUC算法在实际焦平面探测器组件上的实现方法,该方法仅依赖拍摄序列的信息对焦平面探测器的增益和偏置参数进行组间更新或帧间更新,可有效补偿温漂。研制了一种具有自适应非均匀校正功能的非制冷焦平面探测器组件,红外视频经该组件处理后,图像质量有所提高。该组件可明显提高热成像系统的成像性能,并能动态地保证热成像系统随场景变化的稳定性。 相似文献
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由于场景中目标与背景的温差相对较小,红外图像会存在对比度低、视觉效果差的问题,针对这一问题,提出一种基于奇异值非线性修正的红外图像对比度实时增强方法。该方法首先对红外图像进行奇异值分解得到其原始奇异值,然后采用一个对数型非线性变换对图像奇异值进行优化,最后根据修正的奇异值重构出对比度增强的红外图像。利用对数型非线性变换修正图像奇异值不仅能够有效拉伸奇异值的动态范围,同时可优化奇异值的变化梯度,使图像的能量信息得到更充分地表达,改善红外图像不良的视觉效果。实验结果表明,该方法较几种对比方法在视觉效果和客观评价方面均具有更优的增强性能;同时体现出良好的实时性,为实现红外图像的实时增强提供了新途径。 相似文献
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由于场景中目标与背景的温差相对较小,红外图像会存在对比度低、视觉效果差的问题,针对这一问题,提出一种基于奇异值非线性修正的红外图像对比度实时增强方法。该方法首先对红外图像进行奇异值分解得到其原始奇异值,然后采用一个对数型非线性变换对图像奇异值进行优化,最后根据修正的奇异值重构出对比度增强的红外图像。利用对数型非线性变换修正图像奇异值不仅能够有效拉伸奇异值的动态范围,同时可优化奇异值的变化梯度,使图像的能量信息得到更充分地表达,改善红外图像不良的视觉效果。实验结果表明,该方法较几种对比方法在视觉效果和客观评价方面均具有更优的增强性能;同时体现出良好的实时性,为实现红外图像的实时增强提供了新途径。 相似文献
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This paper puts forward a new scene based nonuniformity correction algorithm for IRFPA. This method adopts phase-correlation method for motion estimation and takes the sum of mean-square errors of the pixel brightness between several adjacent frames as the cost function when the brightness constancy assumption between two adjacent frames is satisfied. Nonuniformity correction parameters could be estimated by minimizing such cost function. In order to reduce calculation quantity, we can divide these images into several subblocks, and solve for the optimum solution of the cost function respectively in each subblock. From the analysis, it is shown that the optimum solution is of global uniqueness when all the elements in subblocks could satisfy the ergodicity condition. Then the estimated value of nonuniformity correction parameters could be deduced by minimizing the cost functions. The nonuniformity correction experiments for both infrared image sequence with simulated nonuniformity and infrared imagery with real nonuniformity show that the proposed algorithm could achieve a great correction effect by only analyzing a small number of frames. 相似文献
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We present a novel, stripe nonuniformity correction algorithm for infrared focal plane arrays. This method relies on the separation of nonuniformity and true scene, and the nonuniformity correction parameter is obtained by traversing the error function of two adjacent columns?? pixels in local template window. Based on the succession of two adjacent columns?? correlation, the stripe nonuniformity correction can be achieved in a single frame. Experimental results, to illustrate the performance of the method, include the use of infrared image sequences with simulated nonuniformity and a diverse set of real IR imagery. 相似文献
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Non-uniformity (NU) in infrared images can cause great degradation of the image quality and appearance. Scene-based nonuniformity correction (SBNUC) has become a very effective way to deal with NU. Although many SBNUC methods have been developed by researchers worldwide, few of them have a good correction performance and can be applied to small-package real-time devices. In this paper, we propose a time-domain projection-based registrationscene-based NU correction technology and its detailed hardware realization. We developed a new projection estimator to calculate the relative displacement of neighboring frames. The estimator uses a column and row projection vector to calculate the displacement separately without reducing the accuracy. We also developed an improved gain coefficient correction method using the corrected bias coefficient to correct the gain coefficient by clarifying the intrinsic relationship between these two coefficients instead of correcting them separately. We have also thoroughly analyzed how this technology performs with an actual infrared video sequence containing both low-frequency and high-frequency NUs. The hardware realization of this technology in a single-FPGA-core real-time system is also described in detail. We have successfully realized this technology in a real engineering application. Detailed flow charts for the hardware implementation of this algorithm are also provided. 相似文献
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Influenced by detector materials’ non-uniformity, growth and etching techniques, etc., every detector’s responsivity of infrared focal plane arrays (IRFPA) is different, which results in non-uniformity of IRFPA. And non-uniformity of IRFPA generates fixed pattern noises (FPN) that are superposed on infrared image. And it may degrade the infrared image quality, which greatly limits the application of IRFPA. Non-uniformity correction (NUC) is an important technique for IRFPA. The traditional non-uniformity correction algorithm based on neural network and its modified algorithms are analyzed in this paper. And a new improved non-uniformity correction algorithm based on neural network is proposed in this paper. In this algorithm, the desired image is estimated by using three successive images in an infrared sequence. And blurring effect caused by motion is avoided by applying implicit motion detection and edge detection. So the estimation image is closer to real image than the estimation image estimated by other algorithms, which results in fast convergence speed of correction parameters. A comparison is made to these algorithms in this paper. And experimental results show that the algorithm proposed in this paper can correct the non-uniformity of IRFPA effectively and it prevails over other algorithms based on neural network. 相似文献
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红外人脸图像的边缘轮廓特征对于红外人脸检测、识别等相关应用具有重要价值。针对红外人脸图像边缘轮廓提取时存在伪边缘的问题,提出了一种改进Canny算法的红外人脸图像边缘轮廓提取方法。首先通过对引导滤波算法引入“动态阈值约束因子”替换原始算法中的高斯滤波,解决了原始算法滤波处理不均匀和造成红外人脸图像弱边缘特征丢失的弊端;接着对原始算法的非极大值抑制进行了改进,在原始计算梯度方向的基础上又增加了4个梯度方向,使得非极大值抑制的插值较原始算法更加精细;最后改进OTSU(大津)算法,构造灰度-梯度映射函数确定最佳阈值,解决了原始算法人为经验确定阈值的局限性。实验结果表明:提出的改进Canny算法的红外人脸轮廓提取方法滤波后的图像,相较于原始Canny算法滤波处理,信噪比性能提升了34.40%,结构相似度性能提升了21.66%;最终的红外人脸边缘轮廓提取实验的优质系数值高于对比实验的其他方法,证明改进后的算法对于红外人脸图像边缘轮廓提取具有优越性。 相似文献
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In many infrared imaging systems, the focal plane array is not sufficient dense to adequately sample the scene with the desired field of view. Therefore, there are not enough high frequency details in the infrared image generally. Super-resolution (SR) technology can be used to increase the resolution of low-resolution (LR) infrared image. In this paper, a novel super-resolution algorithm is proposed based on non-local means (NLM) and steering kernel regression (SKR). Based on that there are a large number of similar patches within an infrared image, NLM method can abstract the non-local similarity information and then the value of high-resolution (HR) pixel can be estimated. SKR method is derived based on the local smoothness of the natural images. In this paper the SKR is used to give the regularization term which can restrict the image noise and protect image edges. The estimated SR image is obtained by minimizing a cost function. In the experiments the proposed algorithm is compared with state-of-the-art algorithms. The comparison results show that the proposed method is robust to the noise and it can restore higher quality image both in quantitative term and visual effect. 相似文献