共查询到18条相似文献,搜索用时 404 毫秒
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为提高复杂背景和噪声干扰下红外小目标检测性能,提出了融合深度神经网络和视觉目标显著性的单阶段红外小目标检测算法.首先设计了基于编码器-解码器架构的轻量级全卷积神经网络对红外图像进行分割,实现背景抑制和目标增强;然后利用红外小目标的显著性特征进一步抑制虚警;最后采用自适应阈值法分离出小目标.网络结构中通过引入多个下采样层降低计算量并增大感受野;通过引入多尺度特征提升背景抑制能力;通过引入注意力机制提升模型训练效果.在真实红外图像上的测试表明,本文算法在检测率、虚警率和运算时间等方面都优于典型红外小目标检测算法,适合进行复杂背景下的红外小目标检测. 相似文献
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《光学技术》2021,47(5):594-600
为了提高复杂环境下的红外弱小目标的探测能力,基于人类视觉注意机制,设计了差异直方图耦合显著性映射的弱小目标检测算法。考虑真实目标与其邻域之间的强度差异,通过采用DFT变换来计算红外图像的幅度与相位频谱,以计算二者之间的频谱残差,并联合高斯滤波方法,输出显著性映射,从而有效突出显著区域。分析目标与背景区域的梯度幅度和梯度方向之间的差异,计算红外图像的差异直方图,以充分抑制背景杂波和噪声。联合显著性映射与差异直方图,形成图像对应的融合特征映射。最后,引入自适应阈值分割方法,从融合特征映射中准确定位真实目标。多组测试数据表明,较已有的红外目标检测技术而言,所提算法能够更好地定位出弱小目标,呈现出理想的ROC曲线。 相似文献
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针对目前红外焦平面成像系统在观察目标、特别是极值温差目标时,各温度段灰度描述不均匀和细节不够的问题,提出了一种自适应红外图像双局部增强算法。详细介绍了通过空间分布和灰度统计特性两个方向实现对极值温差图像自适应增强的方法,该方法首先从红外图像的空间分布特性出发,将图像切割成多个局部图像,然后再从直方图灰度分布出发,将局部图像的直方图进行聚类分段,并对分段直方图均衡增强,最后对生成的每个局部图像增强结果进行线性插值拼接完成增强算法。通过在红外焦平面系统中实验证明了极值温差自适应的红外图像双局部增强算法的可行性,并获得了很好的效果,成像质量有明显提高。 相似文献
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海天复杂背景下红外目标的检测跟踪算法 总被引:3,自引:2,他引:1
在分析海天复杂背景下红外目标图像特征的基础上,提出适合该环境的红外目标检测算法.该算法采用行均值相减的方法抑制海平面非线性温度场的影响,并进行中值滤波处理.对于更加复杂的环境,选用数学形态滤波法抑制背景中的大面积云团或海浪,从而确定出目标区域来进行目标图像的分割及增强.同时,综合使用图像捕获区域指定、运动目标检测法、弱目标的增强提取、记忆外推功能、数据融合加权跟踪方法,来保证在海天复杂背景下红外目标的可靠跟踪.实验表明,该算法能较好地处理海天复杂背景下红外目标的检测,且算法易于硬件实现,提高目标检测的实时效率. 相似文献
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基于二代小波变换的红外图像非线性增强算法 总被引:5,自引:1,他引:4
红外图像具有对比度低和信噪比低等特点,实用中必须进行增强处理.将小波分析与模糊逻辑相结合,提出了一种基于二代小波变换的红外图像非线性增强算法.该算法首先利用二代小波变换对图像进行分解,提取图像的多尺度细节特征,然后,根据目标和背景噪声信号的差异,通过模糊非线性增强算子分别对各个分解层的高频子带进行非线性增强来改变目标特征的强度,抑制背景信号,最后利用小波反变换重构图像,以实现图像的对比度增强和背景抑制.与几种常用的图像增强算法实验结果相比,此算法能有效地抑制图像中的背景噪声,增强目标内容信息,取得了较好的增强效果. 相似文献
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Infrared images are characterized by low signal to noise ratio (SNR) and fuzzy texture edges. This article introduces the variational infrared image enhancement algorithm based on gradient field equalization with adaptive dual thresholds. Firstly, we transform the image into gradient domain and get the gradient histogram. Then, we do the gradient histogram equalization. By setting adaptive dual thresholds to qualify the gradients, the image is prevented from over enhancement. The total variation (TV) model is adopted in the reconstruction of the enhanced image to suppress noise. It is shown from experimental results that the image edge details are significantly enhanced, and therefore the algorithm is qualified for enhancement of infrared images in different applications. 相似文献
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Infrared images are characterized by low signal-to-noise ratio and low contrast. Therefore, the edge details are easily immerged in the background and noise, making it much difficult to achieve infrared image edge detail enhancement and denoising. This article proposes a novel method of Gaussian mixture model-based gradient field reconstruction, which enhances image edge details while suppressing noise. First, by analyzing the gradient histogram of noisy infrared image, Gaussian mixture model is adopted to simulate the distribution of the gradient histogram, and divides the image information into three parts corresponding to faint details, noise and the edges of clear targets, respectively. Then, the piecewise function is constructed based on the characteristics of the image to increase gradients of faint details and suppress gradients of noise. Finally, anisotropic diffusion constraint is added while visualizing enhanced image from the transformed gradient field to further suppress noise. The experimental results show that the method possesses unique advantage of effectively enhancing infrared image edge details and suppressing noise as well, compared with the existing methods. In addition, it can be used to effectively enhance other types of images such as the visible and medical images. 相似文献
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提出了一种基于NiosII的红外图像灰度变换设计方案,并详细介绍了红外图像灰度变换算法以及系统硬、软件设计以及实现方法。针对红外图像的特点,提出了一种基于平台直方图均衡的红外图像灰度变换算法,并在CycloneII系列FPGA上实现了该算法,其处理单元采用嵌入式处理器软核NiosII。实验结果表明,该系统能很好地实现红外图形灰度变换,并在一定程度上抑制了背景,提升了目标。 相似文献
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Wang D.Wang M. 《应用光学》2017,(1):106-113
Aiming at solving accuracy problem of infrared small target detection in sky and ocean background scenarios of infrared image sequences, a novel infrared small target detection based on multi-filters algorithm fusion method is presented in this paper. Firstly infrared small target and imaging, time and space characteristics of the corresponding background noise are analyzed. Tophat algorithm with improved Robinson guard filter are then integrated to highlight target and suppress clutter background by using infrared small target imaging features. Adaptive threshold segmentation is used to extract candidate targets, while Unger smoothing filter and multi-objects association filter are used to eliminate random noise and false targets in the candidate targets. Multiple experiments of infrared small target image sequences are implemented, and experimental results show that proposed method can detect infrared small targets at 99% detection rate with high reliability and good real-time performance. © 2017, Editorial Board, Journal of Applied Optics. All right reserved. 相似文献
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In infrared images, detail pixels are easily immerged in large quantity of low-contrast background pixels. According to these characteristics, an adaptive contrast enhancement algorithm based on double plateaus histogram equalization for infrared images was presented in this paper. Traditional double plateaus histogram equalization algorithm used constant threshold and could not change the threshold value in various scenes, so that its practical usage is limited. In the proposed algorithm, the upper and lower threshold value could be calculated by searching local maximum and predicting minimum gray interval and be updated in real time. With the proposed algorithm, the background of infrared image was constrained while the details could also be enhanced. Experimental results proved that the proposed algorithm can effectively enhance the contrast of infrared images, especially the details of infrared images. 相似文献