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作 者:程瑶 贾宁 高晨斐 刘云阳 CHENG Yao;JIA Ning;GAO Chen-fei;LIU Yun-yang(School of Mechanical Engineering,Chongqing University of Technology,Chongqing 400054,China)
出 处:《仪表技术与传感器》2023年第3期77-81,共5页Instrument Technique and Sensor
基 金:国家自然科学基金(51505052);重庆市科委基础与前沿研究一般项目(cstc2016jcyjA0497);重庆理工大学研究生创新项目(clgycx 20202088);重庆市研究生科研创新项目(CYS22653,CYS21464)。
摘 要:针对红外探测器响应串扰导致的图像模糊、图像带有点状或条状串扰等现象,提出一种基于深度卷积神经网络(deep convolutional neural network, DCNN)降噪器的红外串扰图像复原方法。首先从微观载流子扩散原理出发,揭示红外焦平面阵列串扰产生机理,建立电学串扰数学模型及退化模型。接着根据先验知识,建立DCNN降噪器,并结合基于模型的网络优化策略,自适应地训练出适用于红外焦平面串扰模型的DCNN降噪器,从而将受串扰的红外图像恢复为无串扰红外图像。对实验室实测的某国产红外热像仪红外图像进行了复原,实验表明,所提算法能快速复原模糊或带有点状串扰的红外图像,相较于其他红外串扰图像复原方法,图像峰值信噪比(PSNR)、图像结构相似性(SSIM)及运行速度均大幅提升。Aiming that the response crosstalk of infrared detectors can lead to the phenomenon of unclear images,images with dot-like or strip-like crosstalk and so on,a method of infrared crosstalk image restoration based on deep convolutional neural network(DCNN)denoiser was proposed.Firstly,according to the principle of microscopic carrier diffusion,the mechanism of crosstalk generation of infrared focal plane array was revealed,and the mathematical model and degradation model of electrical crosstalk were established.Then according to the prior knowledge,the DCNN denoiser was established.Combined with the model-based network optimization strategy,a DCNN denoiser suitable for the infrared focal plane crosstalk model was adaptively trained,thereby restoring the crosstalk-affected infrared image to a crosstalk-free infrared image.The infrared image of a domestic infrared thermal imager measured in the laboratory was restored.The experiment shows that the proposed algorithm can quickly restore the blurred or point-like crosstalk infrared image.Compared with other infrared crosstalk image restoration methods,the image peak signal-to-noise ratio(PSNR),image structural similarity(SSIM)and running speed are greatly improved.
关 键 词:红外串扰 图像复原 串扰退化模型 DCNN降噪器
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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