摘要
针对遥感影像椒盐噪声处理时,传统算法缺乏有效的噪声区分能力这一问题,提出一种整合了Butterworth滤波技术与灰色关联分析原理的优化去噪方案。运用Butterworth滤波器对噪声图像进行预处理,生成参考图像,再将其与噪声图像进行灰色关联分析,筛选出潜在的噪声像素及其所在局部窗口内受噪声影响的像素,最后对这些像素进行灰色关联度权值校正,从而去除椒盐噪声。实验结果表明,该算法具有更佳噪声去除效果,如在噪声密度为2%~10%的环境下,相比于Butterworth滤波技术,峰值信噪比提高8.8~10.1dB,结构相似度提高约29.7%~33%。整体上,该算法的遥感影像椒盐噪声去除效果优于现有算法,在遥感影像椒盐噪声的去除上,具有一定的应用价值。
In order to solve the problem that traditional algorithms lack effective noise distinguishing ability when processing salt and pepper noise in remote sensing images,an optimized denoising scheme is proposed,which integrates Butterworth filtering technology and grey correlation analysis principle.The Butterworth filter is used to preprocess the noise image to generate the reference image,and then the grey correlation analysis is carried out with above image and identify potentially noisy pixels and those affected by noise within their local windows.Finally,the grey correlation weight of these pixels is corrected to remove the salt and pepper noise.The experimental results show that the proposed algorithm has better noise removal effect.For example,when the noise density is 2%~10%,the peak signal-to-noise ratio is increased by 8.8~10.1dB and the structural similarity is improved by about 29.7%~33%compared with Butterworth filtering technique.On the whole,the effect of this algorithm is better than the existing ones,and it has certain application value in the removal of salt and pepper noise in remote sensing images.
作者
郭海瑞
仉天宇
曹瑞雪
GUO Hairui;ZHANG Tiany;CAO Ruixue(Laboratory for Coastal Ocean Variation and Disaster Prediction,College of Ocean and Meteorology,Guangdong Ocean University,Zhanjiang 524088,China;Key Laboratory of Climate Resources and Environment in Continental Shelf Sea and Deep Ocean(LCRE),Zhanjiang 524088,China;Key Laboratory of Space Ocean Remote Sensing and Application,Ministry of Natural Resources,Zhanjiang 524088,China)
出处
《海洋测绘》
北大核心
2025年第3期65-68,共4页
Hydrographic Surveying and Charting
基金
国家重点研发项目(2021YFC3101801)
国家自然科学基金(92158201)。
