摘要
针对传统的块匹配去噪方法只能处理二维图像的缺点,提出一种基于三维剪切波变换和改进的三维块匹配过滤(block-matching and 4D filtering,BM4D)算法的图像去噪方法。利用三维剪切波变换得到变换域系数,通过硬阈值和维纳滤波,在变换域中实现联合过滤。经过多尺度分解和方向剖分两个滤波阶段,确保三维剪切波变换是局部的;进行硬阈值和维纳滤波,分别包括分组、协同过滤和聚合3个步骤,利用堆积成四维组的体素立方体,在该组的四维变换同时利用每个立方体中体素之间存在的局部相关性和不同立方体中相应体素之间的非局部相关性。通过三维剪切波逆变换,得到每个分组立方体的估计值,在它们的原始位置进行自适应聚合。以峰值信噪比和结构相似度作为评价标准,试验结果表明:该方法不仅能够有效去除高噪声环境下的图像噪声,而且还能够有效地改善图像的视觉效果,具有较高的准确性。
Aimed at the disadvantage that the traditional block matching denoising method could only deal with two-dimensional images, an image denoising method based on 3D shearlet transform and BM4D(block-matching and 4D filtering) was proposed. This method used 3D shearlet transform to obtain transform domain coefficients, and realized joint filtering in transform domain through hard threshold and Wiener filtering stage. The 3D shearlet transformation was localized through two filtering stages: multi-scale decomposition and directional decomposition. The hard threshold and Wiener filtering were performed, which include grouping, collaborative filtering and aggregation. The 4D transformation of the cubes was based on the local correlationandon-local correlation cubes. The estimated values of each grouped cube were obtained by inverse transformation of 3D shearlet transform, and self-adaptive aggregation was performed at their original positions. PSNR(peak signal to noise ratio) and SSIM(structural similarity) were used as evaluation criteria. The results showed that this method could effectively remove image noise in high noise environment, and effectively improved the visual effect of the image with high accuracy.
作者
张胜男
王雷
常春红
郝本利
ZHANG Shengnan;WANG Lei;CHANG Chunhong;HAO Benli(College of Computer Science and Technology,Shandong University of Technology,Zibo 255000,Shandong,China)
出处
《山东大学学报（工学版）》
CAS
CSCD
北大核心
2020年第2期83-90,共8页
Journal of Shandong University（Engineering Science）
基金
国家自然科学基金资助项目(61502282)
山东省自然科学基金资助项目(ZR2015FQ005)
山东省高等学校科技计划资助项目(J18KA362)
山东省智慧矿山信息技术重点实验室开放基金资助项目。
关键词
三维剪切波变换
联合过滤
协同过滤
非局部相关性
自适应聚合
3D shearlet transform
combined filtering
collaborative filtering
non-local correlation
self-adaptive aggregation
