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A multi-scale non-uniformity correction method based on wavelet decomposition and guided filtering for uncooled long wave infrared camera

机译:一种基于小波分解的多尺度非均匀性校正方法,对未冷却的长波红外相机引导滤波

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摘要

In uncooled long-wave infrared (LWIR) imaging systems, non-uniformity of the amplifier in readout circuit will generate significant noise in captured infrared images. This type of noise, if not eliminated, may manifest as vertical and horizontal strips in the raw image and human observers are particularly sensitive to these types of image artifacts. In this paper we propose an effective non-uniformity correction (NUC) method to remove strip noise without loss of fine image details. This multi-scale destriping method consists of two consecutive steps. Firstly, wavelet-based image decomposition is applied to separate the original input image into three individual scale levels: large, median and small scales. In each scale level, the extracted vertical image component contains strip noise and vertical-orientated image textures. Secondly, a novel multi-scale 1D guided filter is proposed to further separate strip noise from image textures in each individual scale level. More specifically, in the small scale level, we choose a small filtering window for guided filter to eliminate strip noise. On the contrary, a large filtering window is used to better preserve image details from blurring in large scale level. Our proposed algorithm is systematically evaluated using real-captured infrared images and the quantitative comparison results with the state-of-the-art destriping algorithms demonstrate that our proposed method can better remove the strip noise without blurring image fine details. (C) 2017 Published by Elsevier B.V.
机译:在未冷却的长波红外(LWIR)成像系统中,读出电路中的放大器的不均匀性将在捕获的红外图像中产生显着的噪声。这种类型的噪声如果未消除,可以在原始图像中的垂直和水平条带上表现为原始图像和人类观察者对这些类型的图像伪影特别敏感。在本文中,我们提出了一种有效的非均匀性校正(NUC)方法,以去除条带噪声而不会损失细图像细节。这种多级腐蚀方法由两个连续步骤组成。首先,应用基于小波的图像分解,将原始输入图像分为三个单独的比例级别:大,中值和小尺度。在每个比例级别中,提取的垂直图像分量包含条带噪声和垂直定向图像纹理。其次,提出了一种新的多尺度1D引导滤波器,以进一步将来自每个单独刻度水平的图像纹理分开的条带噪声。更具体地说,在小规模级别中,我们选择一个用于引导滤波器的小过滤窗口以消除条带噪声。相反,大型过滤窗口用于更好地在大规模水平中从模糊中保持图像细节。我们使用真正捕获的红外图像系统地评估了我们所提出的算法,并且通过最先进的Distriping算法进行定量比较结果表明我们所提出的方法可以更好地去除条带噪声而不模糊图像细节。 (c)2017年由Elsevier B.V发布。

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