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Application of a new method for analyzing images: two-dimensional non-linear additive decomposition

机译：一种新的图像分析方法的应用：二维非线性加性分解

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摘要

This paper documents the application of a new image processing algorithm, two-dimensional non-linear additive decomposition (NLAD), which is used to identify regions in a digital image whose gray-scale (or color) intensity is different than the surrounding background. Standard image segmentation algorithms exist that allow users to segment images based on gray-scale intensity and/or shape. However, these processing techniques do not adequately account for the image noise and lighting variation that typically occurs across an image. NLAD is designed to separate image noise and background from artifacts thereby providing the ability to consistently evaluate images. The decomposition techniques used in this algorithm are based on the concepts of mathematical morphology. NLAD emulates the human capability of visually separating an image into different levels of resolution components, denoted as 'coarse', 'fine', and 'intermediate.' Very little resolution information overlaps any two of the component images. This method can easily determine and/or remove trends and noise from an image. NLAD has several additional advantages over conventional image processing algorithms, including no need for a transformation from one space to another, such as is done with Fourier transforms, and since only finite summations are required, the calculational effort is neither extensive nor complicated.
机译：本文介绍了一种新的图像处理算法的应用，即二维非线性加法分解（NLAD），该算法用于识别数字图像中灰度（或彩色）强度与周围背景不同的区域。存在标准图像分割算法，其允许用户基于灰度强度和/或形状来分割图像。但是，这些处理技术不能充分考虑通常在整个图像上发生的图像噪声和光照变化。 NLAD旨在将图像噪声和背景与伪像分开，从而提供一致地评估图像的能力。该算法中使用的分解技术基于数学形态学的概念。 NLAD模仿了人类将图像可视化分为不同级别的分辨率分量（称为“粗略”，“精细”和“中间”）的能力。很少的分辨率信息会与任何两个分量图像重叠。该方法可以容易地确定和/或去除图像中的趋势和噪声。与传统的图像处理算法相比，NLAD具有其他优势，包括不需要从一个空间转换到另一个空间（例如通过傅立叶变换完成的转换），并且由于只需要有限的求和，因此计算工作既不会广泛也不复杂。

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