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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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采用虚拟仪器技术,设计了基于LabVIEW软件平台的热释电IRFPA(红外焦平面阵列)非均匀性校正系统。该系统可通过控制面板选择校正算法,选取3种不同的标准像元的标准校正曲线。可以对IRFPA待校正的像元输出进行采集,对非均匀性参数进行测试,还可以进行非均匀性校正。系统通过三维波形以及图像的显示来观察校正前后对比,并能计算出校正前后的NU(非均匀性)值大小。对像元数为120160的热释电IRFPA输出的视频信号进行了非均匀性校正实验,对非均匀性校正算法进行了统计对比,对仿真结果及数据进行了分析总结。经过校正实验验证了系统的可行性,结果证明基于平均值法的两点定标算法对热释电IRFPA非均匀性校正后的NU值是最小的。通过统计数据得出校正后的NU可平均下降30%。 相似文献
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Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas. 相似文献
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Non-uniformity correction is the key issue for the image quality improvement of infrared focal panel array (IRFPA) imaging. A non-uniformity correction (NUC) algorithm for IRFPA based on motion controllable micro-scanning platform and perimeter diaphragm strips is presented. We initially execute one-point calibration to the perimeter detectors, then based on controllable motion of adjacent frames, a special algebraic algorithm is proposed to transport the calibration of the perimeter detectors to those interior un-corrected ones. In this way, the bias parameter of the whole field of view (FOV) is calculated. The algorithm can be easily combined with sub-pixel imaging, thereby improving the quality of thermal imaging system (image spatial resolution and uniformity). All calculations are algebraic, with a low computation load. The algorithm can realize adaptive one point calibration without covering the central FOV rapidly. Experiments on simulated infrared data demonstrate that this algorithm requires only dozens of frames to obtain high quality corrections. 相似文献
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基于干扰对消的红外焦平面非均匀性校正算法 总被引:1,自引:1,他引:0
红外焦平面器件的非均匀性产生机理复杂,难以准确拟合探测元响应曲线。提出了一种基于相关干扰抵消的非均匀性校正算法,以预先采集到的一帧黑体面源图像做为自适应干扰对消器的参考输入图像,自适应滤波器由参考输入图像迭代计算出待校正红外图像的空间噪声的最佳估计,实现从空间噪声中提取真实图像信号。自适应滤波算法采用变步长最小均方误差算法,减少了算法的运算量,提高了算法的收敛速度。理论分析以及针对实际红外图像的仿真结果表明,提出的算法校正效果好,收敛速度快,更易于工程实现。 相似文献
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针对应用常规红外图像非均匀性校正方法在变积分时间时,图像灰度值会发生改变的现象,提出了一种适应积分时间调整的红外图像非均匀性校正方法.该方法将不同积分时间、不同温度的黑体定标数据和对应的理论红外辐射量整合为一个整体数据库,借助神经网络损失函数和误差反向传递机制,对模型中的校正系数进行学习.训练得到的校正网络能在红外相机积分时间实时调整过程中,保证图像均匀地稳定输出,对后端红外图像处理有着重要意义,并验证训练该网络不需要大量定标数据.而针对红外探测器响应漂移的现象,则提出了在线修正校正系数的方法以有效应对. 相似文献
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针对两点温度定标算法在应用过程中曝露的问题,提出了基于变积分时间的红外焦平面非均匀性校正算法.该算法先对图像进行非线性压缩,转换为线性图像,再利用红外焦平面阵列探测元的响应特性与积分时间之间的关系,采用改变积分时间的方法拟合红外焦平面探测器的平均响应特性曲线,进行两点校正,然后对结果进行取指数操作,即得到原图非均匀校正后的图像.分别利用两点温度定标法和变积分法对航拍红外图像进行校正效果验证,同时进行了不同校正算法的非均匀性适应性评价实验.实验结果表明新算法计算量小,校正准确度高,反应速度快,并在一定程度上解决了大动态范围下响应非线性对校正性能的影响,具有很好的工程应用价值. 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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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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Scene-based non-uniformity correction (SBNUC) algorithms are an important part of infrared image processing; however, SBNUC algorithms usually cause two defects: (1) ghosting artifacts and (2) over-correction. In this paper, we use the absolute difference based on guided image filter (AD-GF) method to validate the performance of SBNUC algorithms. We obtain a self-separation source using the improved guided image filter to process the input image, and use the self-separation source to obtain the space-high-frequency parts of the input image and the corrected image. Finally, we use the absolute difference between the two space-high-frequency parts as the evaluation result. Based on experimental results, the AD-GF method has better robustness and can validate the performance of SBNUC algorithms even if ghosting artifacts or over-correction occur. Also the AD-GF method can measure how SBNUC algorithms perform in the time domain, it’s an effective evaluation method for SBNUC algorithm. 相似文献
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This paper proposes a kind of pipelined electric circuit architecture implemented in FPGA, a very large scale integrated circuit (VLSI), which efficiently deals with the real time non-uniformity correction (NUC) algorithm for infrared focal plane arrays (IRFPA). Dual Nios II soft-core processors and a DSP with a 64+ core together constitute this image system. Each processor undertakes own systematic task, coordinating its work with each other’s. The system on programmable chip (SOPC) in FPGA works steadily under the global clock frequency of 96Mhz. Adequate time allowance makes FPGA perform NUC image pre-processing algorithm with ease, which has offered favorable guarantee for the work of post image processing in DSP. And at the meantime, this paper presents a hardware (HW) and software (SW) co-design in FPGA. Thus, this systematic architecture yields an image processing system with multiprocessor, and a smart solution to the satisfaction with the performance of the system. 相似文献