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An Infrared Image Enhancement Algorithm for Gas Leak Detecting Based on Gaussian Filtering and Adaptive Histogram Segmentation

机译:基于高斯滤波和自适应直方图分割的气体泄漏检测红外图像增强算法

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Gas leak detection technology using infrared imaging provides significant advantages in detection range, efficiency and dynamic image visualization. Enhancing gas leak traces is essential to the performance of infrared image detection systems, as raw images tend to suffer from low contrast. However, traditional infrared image enhancement methods present drawbacks in the enhancement of gas leak traces. In this paper, a method for image enhancement using Gaussian filtering and adaptive histogram segmentation is proposed. This method consists of three steps: image layering, background and detail layer processing and image fusion output. The experimental results indicate that the proposed method obtained encouraging results in gas leak trace enhancement and de-noising. Compared with the global Adaptive Histogram Segmentation (AHS) algorithm, the performance of the proposed approach output confirmed the superiority of the proposed algorithm.
机译:使用红外成像的气体泄漏检测技术在检测范围,效率和动态图像可视化方面提供了显着的优势。 增强气体泄漏迹线对于红外图像检测系统的性能至关重要,因为原始图像倾向于遭受低对比度。 然而,传统的红外图像增强方法在增强气体泄漏迹线中存在缺点。 在本文中,提出了一种使用高斯滤波和自适应直方图分割的图像增强方法。 此方法由三个步骤组成:图像分层,背景和详细层处理和图像融合输出。 实验结果表明,该方法获得了令人鼓舞的气体泄漏痕量增强和去噪。 与全局自适应直方图分割(AHS)算法相比,所提出的方法输出的性能证实了所提出的算法的优越性。

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