Neighbourhood Based Bi-Level Contrast Adjustment for Underwater Image Enhancement Using Modified Particle Swarm Optimization

S. Paul, S. De, Sandip Dey
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引用次数: 0

Abstract

This paper presents a neighbourhood based bi-level contrast adjustment algorithm for underwater image enhancement. In this algorithm, at the outset, the histogram of the images is divided into two equal parts. A Modified Particle Swarm Optimization (MPSO) is introduced in the proposed algorithm to find two different points in each part of the histogram such that each part of the histogram can be separately stretched on the basis of these points. The quality of the output images (enhanced images) is visually and quantitatively judged with reference to the best fitness, mean fitness, Peak Signal to Noise Ratio (PSNR), Underwater Image Quality Measure (UIQM), average PSNR and average UIQM values of all test images and Friedman test. The acquired results proves that there is a substantial improvement of the proposed algorithm compared to others.
基于邻域双水平对比度调整的改进粒子群优化水下图像增强
提出了一种基于邻域的双水平对比度调整算法,用于水下图像增强。在该算法中,首先将图像的直方图分成两个相等的部分。该算法引入了改进粒子群优化算法(MPSO),在直方图的每个部分中找到两个不同的点,从而可以在这些点的基础上分别拉伸直方图的每个部分。根据最佳适应度、平均适应度、峰值信噪比(PSNR)、水下图像质量度量(UIQM)、所有测试图像的平均PSNR和平均UIQM值以及Friedman检验,从视觉上定量判断输出图像(增强图像)的质量。实验结果表明,与其他算法相比,本文算法有很大的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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