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基于稀疏表示的红外图像去噪算法研究
投稿时间:2018-08-16  修订日期:2018-08-29  点此下载全文
引用本文:何培亮.基于稀疏表示的红外图像去噪算法研究[J].红外,2018,39(10):27~32
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作者单位E-mail
何培亮 重庆邮电大学 1831975981@qq.com 
基金项目:国家自然科学基金项目(61671094);重庆市科委项目(CSTC2015JCYJA40032)
中文摘要:红外图像具有动态范围窄、对比度低、易受噪声污染等缺点,传统红外图像去噪算法在去除噪声的同时也滤掉了图像细节。提出了一种基于稀疏表示的红外图像去噪新方法。该方法首先将原始红外图像进行聚类分析,再将每一聚类子图像分解成字典,由稀疏系数矩阵重构去噪后的红外图像。实验结果表明,该方法相比于传统红外图像去噪算法,能更好地保留图像的细节信息,视觉效果比较理想。
中文关键词:红外图像  稀疏表示  去噪
 
Study of Infrared Image Denoising Algorithm Based on Sparse Representation
Abstract:Infrared images have the disadvantages of narrow dynamic range, low contrast and being easy to be polluted by noise. However, traditional infrared image denoising algorithms may filter out image details while removing noise. A new infrared image denoising method based on sparse representation is proposed. The method firstly clusters an original infrared image; secondly decomposes each cluster sub-image into a dictionary; and then the denoised infrared image is reconstructed from the sparse coefficient matrix. The experimental results show that this method can retain image details better than the traditional infrared image denoising algorithm and the visual effect is ideal.
keywords:infrared image  sparse representation  denoising
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