论文标题
使用非本地滤波从多光谱图像重建的高光谱图像重建
Hyperspectral Image Reconstruction from Multispectral Images Using Non-Local Filtering
论文作者
论文摘要
使用光谱是许多应用中的重要元素，例如在材料分类中。通常，这些信息是通过使用高光谱摄像机获得的。不幸的是，这些摄像机有一些主要的缺点，例如无法录制视频。因此，使用带有宽频过滤器的多光谱摄像机，这些相机便宜得多，并且通常能够捕获视频。但是，使用多光谱摄像机需要一个额外的重建步骤来产生光谱信息。通常，必须在存在成像噪声的情况下完成此重构步骤，这会严重降解重建光谱。通常，在整个图像上发现相同或相似的像素，具有独立噪声的优势。与最新的光谱重建方法相反，该方法仅通过基于块的处理来利用相邻像素，本文在光谱重建中引入了非本地滤波。首先，块匹配过程找到类似的非本地多光谱块。此后，高光谱像素是通过使用重建Wiener滤波器协作过滤匹配的多光谱像素来重建的。提出的新型程序甚至在非常强烈的噪音下工作。与最先进的方法相比，该方法能够将光谱角度降低至18％，并将峰值信噪比提高到1.1db。此外，视觉结果更具吸引力。
Using light spectra is an essential element in many applications, for example, in material classification. Often this information is acquired by using a hyperspectral camera. Unfortunately, these cameras have some major disadvantages like not being able to record videos. Therefore, multispectral cameras with wide-band filters are used, which are much cheaper and are often able to capture videos. However, using multispectral cameras requires an additional reconstruction step to yield spectral information. Usually, this reconstruction step has to be done in the presence of imaging noise, which degrades the reconstructed spectra severely. Typically, same or similar pixels are found across the image with the advantage of having independent noise. In contrast to state-of-the-art spectral reconstruction methods which only exploit neighboring pixels by block-based processing, this paper introduces non-local filtering in spectral reconstruction. First, a block-matching procedure finds similar non-local multispectral blocks. Thereafter, the hyperspectral pixels are reconstructed by filtering the matched multispectral pixels collaboratively using a reconstruction Wiener filter. The proposed novel procedure even works under very strong noise. The method is able to lower the spectral angle up to 18% and increase the peak signal-to-noise-ratio up to 1.1dB in noisy scenarios compared to state-of-the-art methods. Moreover, the visual results are much more appealing.
