一种改进的三维块匹配图像去噪算法  

An Improved Block Matching 3D Filtering Image Denoising Algorithm

作　　者：汪祖辉[1] 孙刘杰[1] 邵雪[1] 

机构地区：[1]上海理工大学,上海200093

出　　处：《包装工程》2016年第21期198-203,共6页Packaging Engineering

基　　金：上海市教委科研创新重点资助项目(13ZZ111)

摘　　要：目的为了有效消除噪声图像中的椒盐噪声、高斯噪声甚至混合噪声,改进三维块匹配算法,提出一种新的图像去噪算法。方法首先,该算法将含噪声图像用图像块之间的相似性构建三维矩阵。然后,在图像块之间进行硬阈值滤波降低噪声,对图像块集合加权平均重建得到初步估计去噪图像。最后,对初步估计结果图像进行块匹配,在图像块内和图像块之间进行维纳滤波和加权中值滤波,得到最终去噪图像。结果仿真结果表明,该算法对图像采集的常见噪声均表现出理想的去噪效果,PSNR值均大于31 d B。对比维纳滤波、中值滤波、硬阈值小波滤波,文中算法对高斯噪声、椒盐噪声和混合噪声的去噪结果 PSNR值为31.5334~36.6466 d B,均高于其他算法,最高差值达到12.08 d B。结论结合中值滤波和三维块匹配算法的图像去噪算法,能够较好去除噪声图像的多种类型噪声,是一种较为优秀的去噪算法。In order to effectively eliminate noise image with impulse noise, Gaussian noise and even mixed noise, the work improves the 3D block-matching algorithm and puts forward a new image denoising algorithm. Firstly, the 3D ma- trix was constructed with the similarity between image block of noise image. Then, the noise was attenuated by hard-thresholding between the image blocks, and the initially estimated denoised images were obtained through the weighted average reconstruction of image blocks. Finally, block-matching was performed on the initially estimated de- noised images, and Wiener filtering and weighted median filtering were conducted in and among image blocks, finally denoised images were obtained. Simulation results showed that the proposed algorithm had ideal denoising effect on common noise of image acquisition, and the PSNR value was more than 31 dB. Compared with Wiener filtering, median filtering and hard threshold wavelet filtering, PSNR results of Gaussian noise, impulse noise and mixed noise with this algorithm were 31.5334 ~ 36.6466 dB, higher than other algorithms. The highest difference value reached 12.08 dB. In conclusion, image denoising method combined with median filter and 3D block-matching algorithm can better reduce a variety of noises and is an excellent denoising algorithm.

关 键 词：图像去噪 块匹配 三维变换 中值滤波 

分 类 号：TS801.3[轻工技术与工程]