计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
10期
156-158
,共3页
二维经验模态分解(BEMD)算法%自适应维纳滤波算法%降噪
二維經驗模態分解(BEMD)算法%自適應維納濾波算法%降譟
이유경험모태분해(BEMD)산법%자괄응유납려파산법%강조
Bidimensional Empirical Mode Decompositior(BEMD)method%adaptive wiener method%denoise
自适应维纳滤波器是一种经典的线性降噪滤波器,较其他线性滤波器能够更好地解决边界模糊的问题.然而由于含噪图像的噪声主要集中于它的高频部分,而图像的低频部分所含有的噪声较高频部分则小很多.自适应维纳滤波算法对图像中所有频率成份都不加区分地进行滤波降噪处理,因而它不能得到更为令人满意的结果.提出了一种将二维经验模态分解和自适应维纳滤波相结合的图像去噪方法,通过将图像分解为不同频率成份的子图像并对各子图像采用不同的降噪处理,从而更好地对含噪图像进行降噪.实验结果表明,算法相对于自适应维纳滤波算法降噪效果更好.
自適應維納濾波器是一種經典的線性降譟濾波器,較其他線性濾波器能夠更好地解決邊界模糊的問題.然而由于含譟圖像的譟聲主要集中于它的高頻部分,而圖像的低頻部分所含有的譟聲較高頻部分則小很多.自適應維納濾波算法對圖像中所有頻率成份都不加區分地進行濾波降譟處理,因而它不能得到更為令人滿意的結果.提齣瞭一種將二維經驗模態分解和自適應維納濾波相結閤的圖像去譟方法,通過將圖像分解為不同頻率成份的子圖像併對各子圖像採用不同的降譟處理,從而更好地對含譟圖像進行降譟.實驗結果錶明,算法相對于自適應維納濾波算法降譟效果更好.
자괄응유납려파기시일충경전적선성강조려파기,교기타선성려파기능구경호지해결변계모호적문제.연이유우함조도상적조성주요집중우타적고빈부분,이도상적저빈부분소함유적조성교고빈부분칙소흔다.자괄응유납려파산법대도상중소유빈솔성빈도불가구분지진행려파강조처리,인이타불능득도경위령인만의적결과.제출료일충장이유경험모태분해화자괄응유납려파상결합적도상거조방법,통과장도상분해위불동빈솔성빈적자도상병대각자도상채용불동적강조처리,종이경호지대함조도상진행강조.실험결과표명,산법상대우자괄응유납려파산법강조효과경호.
Adaptive wiener filter, a classical linear denoising filter, can solve the problem of fuzzy boundary more effectively than other linear filter. However, the noise mainly exists in high frequency part of a picture, and much less in the part of low frequency, while adaptive wiener filter tends to deal with the noise without considering the different frequency componets of the picture, therefore, it can not get a satisfactory result. A new method combining BEMD method and adaptive wiener filter is proposed in this paper, which can denoise better through decomposing a picture into different part accoding to the different frequency and then denoising seperately. The result of the experiment in this paper shows that the proposed method performs much better than adaptive wiener filter.