一种新的双密度复小波域图像去噪算法

计算机科学 ›› 2011, Vol. 38 ›› Issue (3): 292-294.

一种新的双密度复小波域图像去噪算法

袁博，尚赵伟，郎方年

(重庆大学计算机学院重庆400030);(中国人民解放军77675部队林芝860000);(四川省模式识别与智能信息处理重点实验室成都610106)

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家自然基金项目重点基金(90820306)而上基金(60873092)，教育部高等学校博士学科点专项科研基金项目(20060611009),重庆市自然科学基金重点项目(CSTC2007BA2003)资助。

Image Denoising Using Double-density Complex Wavelet Transform

YUAN Bo,SHANG Zhao-wei,LANG Fang-nian

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 提出一种改进的基于双密度复小波系数组合的图像去噪算法。采用双密度复小波分解噪声图像，将其变换系数按规则重新排列组合，增强了图像的边缘信息。引入贝叶斯最大后验佑计理论下的双变量模型，充分挖掘其系数尺度内和尺度间的双重关联性，有效地提高了去噪性能。仿真实验表明，去噪后的图像克服了常见的伪吉布斯现象，与当前一些图像去噪算法相比较，其客观评价指标PSNR以及去噪后图像的主观视觉效果都有明显的提高和改善，且有效地保留了原始图像的纹理和细节信息。

关键词: 图像降噪，双密度复小波，贝叶斯估计，双变量模型

Abstract: This paper proposed a new image denoising method based on the DoublcDensity Complex Wavelet I}ransform. The proposed algorithm uses the Double-Density Complex Wavelet Transform to decompose the denosied image,and permutes the decomposition coefficient to stress the edge information. The I3ivariate model under the maximum estimate of 13aycsian can exploit the intra scale and inter-scale corrclations of coefficients. Compared with some current outstanding denoising methods,the simulation results and analysis show that the proposed algorithm obviously outperforms in both Pcak Signal-to-Noise Ratio(PSNR) and visual quality, and effectively preserves detail and texture information of original images.

Key words: Image denosing, Double-density complex wavelet transform, Bayesian estimation, Bivariate model

袁博，尚赵伟，郎方年. 一种新的双密度复小波域图像去噪算法[J]. 计算机科学, 2011, 38(3): 292-294. https://doi.org/

YUAN Bo,SHANG Zhao-wei,LANG Fang-nian. Image Denoising Using Double-density Complex Wavelet Transform[J]. Computer Science, 2011, 38(3): 292-294. https://doi.org/

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