Image enhancement using the image sharpening, contrast enhancement, and Standard Median Filter (Noise Removal) with pixel-based and human visual system-based measurements

Erwin, Adam Nevriyanto, D. Purnamasari
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引用次数: 19

Abstract

In this paper, we explained the three methods of image enhancement: Image Sharpening by sharpening the edges, Contrast Enhancement using Standard Histogram Equalization and Standard Median Filtering where noise is filtered using these methods first and finally noise is eliminated. Then we put on the measurement parameters using a calculation based on the image quality of the pixel MSE and PSNR and calculations based on human vision system (HVS) that SSIM. The dataset we use is BSDS300 Berkeley and the environment is Matlab 2016a. We can state that the image quality measurement is good where the results are accurate so that we can determine the best methods too. We got SSIM value is close to 1 and the value obtained MSE and PSNR is minimum in Image Sharpening which is mean Image Sharpening is best of basic methods in Image Enhancement.
图像增强使用图像锐化,对比度增强和标准中值滤波器(噪声去除)与基于像素和人类视觉系统的测量
在本文中,我们解释了图像增强的三种方法:通过锐化边缘来增强图像,使用标准直方图均衡化和标准中值滤波来增强对比度,其中首先使用这些方法过滤噪声,最后消除噪声。然后通过计算基于图像质量的像素的MSE和PSNR以及基于人眼视觉系统(HVS)的SSIM来确定测量参数。我们使用的数据集是BSDS300 Berkeley,环境是Matlab 2016a。我们可以说图像质量测量是好的,结果是准确的,这样我们就可以确定最好的方法。图像锐化得到的SSIM值接近于1,得到的MSE和PSNR值最小,是图像增强的基本方法中效果最好的。
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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