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A Vignetting Correction Algorithm for Bright-Field Microscopic Images of Activated Sludge

机译:活性污泥明场显微图像的渐晕校正算法

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Vignetting correction is an essential part of pre-processing to address decreasing illumination towards the border of a microscopic image. Image processing and analysis is a potential tool for monitoring and prediction of activated sludge wastewater treatment plant. Microscopic images of activated sludge require anti-vignetting or vignetting correction algorithm for accuracy of subsequent image analysis procedures. In this paper, we proposed a vignetting correction procedure based on Gaussian modeling. The Gaussian model is estimated by using Otsu threshold and iterative evaluation of the model. The advantage of the proposed algorithm is that the vignetting model estimated for one image is found to be valid for all other images irrespective of illumination of microscope. Once the model is calibrated, the correction becomes simple addition, making the proposed technique time-efficient compared to state-of-the-art procedures. The assessment was done subjectively and by using segmentation. The proposed procedure performed better than polynomial approximation and comparable to Leong's algorithm.
机译:渐晕校正是预处理的重要组成部分,用于解决朝着显微图像边界的照明减少的问题。图像处理和分析是监测和预测活性污泥废水处理厂的潜在工具。活性污泥的微观图像需要反渐晕或渐晕校正算法,以确保后续图像分析程序的准确性。在本文中,我们提出了一种基于高斯建模的渐晕校正程序。通过使用Otsu阈值和对该模型的迭代评估来估计高斯模型。提出的算法的优点是,发现一个图像估计的渐晕模型对于所有其他图像都是有效的,而与显微镜的照明无关。校准模型后,校正变得简单易行,与最先进的程序相比,该技术节省了时间。评估是主观的,并且使用了细分。所提出的过程比多项式逼近要好,并且可以与Leong算法相提并论。

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