计算机技术与发展2018，Vol.28Issue(5)：9-12,4.DOI:10.3969/j.issn.1673-629X.2018.05.003
基于权重自适应形态学的周期性噪声去除方法
A Periodic Noise Elimination Algorithm Based on Morphological Filtering with Auto-adapted Weights
摘要
Abstract
Aiming at the problem that it is easy to cause the image distortion or poor noise reduction while eliminating the period noise, we propose a periodic denoising method based on morphological filtering with auto-adapted weights.In this method,periodic noise of im-ages is processed serially by structural elements of different scales,then the results of serial processing are processed in parallel by con-structing composite cascade filter using multi-structural elements.In order to verify the denoising performance of the proposed algorithm, some denoising algorithms are used to eliminate periodic noise and mixed noise.The experiments show that the de-noised image obtained by the proposed algorithm is less residual noise and clearer textures than other algorithms visually.At the same time,in the quantitative e-valuation standard,the PSNR and SSIM of de-noised image obtained by the proposed algorithm are higher.So,it is robust,not only ef-fectively restraining periodic and mixed noise,but also preferably maintaining image geometry characteristic.关键词
周期性噪声/图像去噪/自适应权重/形态滤波Key words
periodic noise/image denoising/auto-adapted weights/morphological filter分类
信息技术与安全科学引用本文复制引用
戴丹,张兴刚..基于权重自适应形态学的周期性噪声去除方法[J].计算机技术与发展,2018,28(5):9-12,4.基金项目
贵州省科技合作计划项目(20157641) （20157641）
贵州大学"本科教学工程"建设项目(JG201623) （JG201623）
