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Particle swarm optimized bi-histogram equalization for contrast enhancement and brightness preservation of images

机译:粒子群优化的双直方图均衡化,可增强对比度和图像亮度

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摘要

A novel technique, Optimized Bi-Histogram Equalization (OBHE), is proposed in this paper for preserving brightness and enhancing the contrast of any input image. The central idea of this technique is to first segment the histogram of the input image into two, based on its mean and then weighting constraints are applied to each of the sub-histograms separately. Those two histograms are equalized independently and their union produces a brightness-preserved and contrast-enhanced output image. While formulating the weighting constraints, Particle Swarm Optimization (PSO) is employed to find the optimal constraints in order to maximize the degree of brightness preservation and contrast enhancement. This technique is found to have an edge over the other contemporary methods in terms of Entropy and Absolute Mean Brightness Error.
机译:为了保持亮度并增强任何输入图像的对比度,本文提出了一种新颖的技术,即最佳双直方图均衡化(OBHE)。该技术的中心思想是,首先根据输入图像的直方图的平均值将其直方图分为两部分,然后将权重约束分别应用于每个子直方图。这两个直方图被独立地均衡,并且它们的并集产生了亮度保持和对比度增强的输出图像。在制定加权约束条件时,采用粒子群优化(PSO)查找最佳约束条件,以最大程度地保持亮度和增强对比度。发现该技术在熵和绝对平均亮度误差方面比其他当代方法更具优势。

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