基于Dual-Tree CWT和自适应双边滤波器的图像去噪算法

作者:崔金鸽;陈炳权;徐庆 刊名:计算机工程与应用 上传者:梅黎峰

【摘要】针对目前图像去噪方法主要局限于单一噪声,无法有效解决多种混合噪声的不足,提出了一种基于Dual-Tree CWT和自适应双边滤波器的图像去噪算法.该算法使用双树复小波变换对含噪图像进行多尺度和多方向的分解,由改进阈值对各个方向子带的高频系数进行阈值量化,同时由自适应双边滤波对每尺度下低频子带系数进行滤波,并将重构得到的图像进一步去除噪声.实验仿真结果表明,该方法对混合噪声的滤除效果明显优于现有算法,且能较好地保护图像的边缘细节信息,通过客观评价指标峰值信噪比(PSNR)和均方根误差(RMSE)定量比较,PSNR提升了5.3332~6.5278 dB,RMSE可降低29.41%~46.03%,运行时间仅为1.4920 s,整体降噪性能更优.

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2018,54(18) 1 引言 采集图像时,往往因为客观因素(如光照)或者主观因素(如人)会给图像添加部分噪声,使得原始图像的细节和边缘信息不能完整保留,严重影响图像的使用价值及后续处理的可行性。目前图像去噪方法主要包括空域滤波法和频域滤波法,中值滤波器[1]、维纳滤波器[2]、 双边滤波器[3]等是空域滤波的常见算法,其中中值滤波对于乘性噪声有较好的滤除效果,尤其是椒盐噪声;后者主要常见算法有小波离散变换(DWT)[4]、双树复小波变换(Dual-Tree CWT)[5-8]和Contourlet变换[9]等,该类方法往往先对图像进行多尺度和不同方向上的分解,然后根据建立的模型对频域的系数进行处理来去除噪声。 基于Dual-Tree CWT和自适应双边滤波器的图像去噪算法 崔金鸽1,陈炳权1,2,徐 庆1 CUI Jinge1, CHEN Bingquan1,2, XU Qing1 1.吉首大学 物理与机电工程学院,湖南 吉首 416000 2.湖南大学 电气与信息工程学院,长沙 410082 1.College of Physics and Electromechanical Engineering, Jishou University, Jishou, Hunan 416000, China 2.College of Electrical and Information Engineering, Hunan University, Changsha 410082, China CUI Jinge, CHEN Bingquan, XU Qing. Image denoising algorithm based on Dual-Tree CWT and adaptive bilateral filtering. Computer Engineering and Applications, 2018, 54(18):223-228. Abstract:For the current image denoising methods are mainly limited to the single noise, can not effectively solve the lack of the various mixed noise, an image denoising algorithm based on Dual-tree CWT and adaptive bilateral filtering is proposed. The algorithm uses the Dual-tree CWT to decompose the noisy image in multi-scales and multi-directions. The high-frequency coefficients of the sub-bands in each direction are quantized by the improved threshold, the low-frequency sub-band coefficients are filtered simultaneously by the adaptive bilateral filtering in each scale, and the reconstructed image is further removed. Experimental results show that the prop

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