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Establishment of pre-processing station for denoising NOAA satellite images using Legendre Fenchel transformation method

机译:建立Legendre Fenchel变换法去噪NOAA卫星图像的预处理站

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The main focus of this paper lies in establishing a preprocessing (particularly for denoising) station for National Oceanic and Atmospheric Administration (NOAA) satellite images. Main theme behind this establishment is to make use of new algorithms for the purpose of denoising satellite's images. This paper makes use of total variation method based denoising and Legendre Fenchel transformation (ROF) based denoising for the removal of unwanted pixel information from an image. Further, this paper identifies that Legendre Fenchel ROF model suits better in denoising. The reason behind this identification is because of its computational speed while denoising an image without deteriorating the quality of an image. This paper uses the newly designed turnstile antenna for capturing signals (WXtoImg software is used in converting APT (Automatic Picture Transmission) signal of NOAA to an image) from NOAA satellite. Results are scrutinized and validated through various quality metrics. This station aims at solving the issue of denoising at less process time without degrading the quality of an image by the use of Legendre Fenchel ROF model.
机译:本文的主要重点在于为国家海洋和大气管理局(NOAA)卫星图像建立预处理站(尤其是用于降噪)。该机构背后的主题是利用新算法对卫星图像进行去噪。本文利用基于总变化法的降噪和基于勒让德·芬谢尔变换(ROF)的降噪技术从图像中去除不需要的像素信息。此外,本文确定了Legendre Fenchel ROF模型更适合去噪。该识别背后的原因是由于其在对图像进行去噪的同时不降低图像质量的计算速度。本文使用新设计的旋转天线来捕获来自NOAA卫星的信号(使用WXtoImg软件将NOAA的APT(自动图像传输)信号转换为图像)。通过各种质量指标对结果进行审查和验证。该工作站旨在通过使用Legendre Fenchel ROF模型来解决在更少的处理时间上降噪的问题,而不会降低图像质量。

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