改进的神经网络非均匀校正算法的研究与实现  

Research and Implementation of Improved Neural Network NonUniformity Correction Algorithm

作　　者：段程鹏[1] 刘伟[1] 谢庆胜[1] 冷寒冰[1] 易波[1] 陈耀宏[1] 

机构地区：[1]中国科学院西安光学精密机械研究所,陕西西安710119

出　　处：《光学学报》2015年第7期106-112,共7页Acta Optica Sinica

摘　　要：为了实现红外图像的非均匀校正,目前通常采用神经网络法完成红外图像的自适应非均匀校正,此方法能够自动完成校正系数的更新,但算法复杂,校后输出图像对比度不高,出现重影和图像模糊的现象。采用改进的神经网络非均匀校正方法,利用高低温黑体求得校正系数初始值,且对算法迭代过程输出期望值的计算做了改进。测试结果表明,该方法简单实用,具有较好的非均匀校正效果。In order to implement non-uniformity correction of infrared image, the neural network algorithm is commonly used to complete adaptive non-uniformity correction of infrared image at present, this method can update the correction factor automatically, but the algorithm is complex, and the correction output image contrast is low, the ghosting and image blurring appears. An improved neural network non-uniformity correction method is presented, the initial value of calibration coefficients are obtained by the use of high and low temperature blackbody, and the calculation of the output expect value in the iterative process is improved.The test results show that the method is simple and practical, and has a good non-uniform correction effect.

关 键 词：图像处理 神经网络法 非均匀校正 最陡下降法 高低温黑体 

分 类 号：TN215[电子电信—物理电子学]