Stacked denoising autoencoder for infrared thermography image enhancement

Ziang Wei, H. Fernandes, J. Tarpani, A. Osman, X. Maldague
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Abstract

Pulsed thermography is one of the most popular thermography inspection methods. During an experiment of pulsed thermography, a specimen is quickly heated, and infrared images are captured to provide information about the specimen’s surface and subsurface conditions. Adequate transformations are usually performed to enhance the contrast of the thermal images and to highlight the abnormal regions before these thermal images are visually inspected. Given that deep neural networks have been a success in computer vision in the past few years, a data contrast enhancement approach with stacked denoising autoencoder (DAE) is proposed in this paper to enhance the abnormal regions in the thermal frames gathered by pulsed thermography. Compared to the direct principal component thermography, the proposed method can enhance the abnormalities evidently without weakening important details.
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用于红外热成像图像增强的层叠去噪自编码器
脉冲热成像是最常用的热成像检测方法之一。在脉冲热成像实验中,试样被快速加热,红外图像被捕获,以提供有关试样表面和地下条件的信息。在目视检查这些热图像之前,通常进行适当的变换以增强热图像的对比度并突出异常区域。鉴于近年来深度神经网络在计算机视觉领域取得的成功,本文提出了一种基于叠置去噪自编码器(DAE)的数据对比度增强方法,以增强脉冲热成像采集的热帧中的异常区域。与直接主成分热像法相比,该方法能在不弱化重要细节的前提下明显增强异常。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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