中国光学
中國光學
중국광학
CHINESE JOURNAL OF OPTICS
2013年
6期
876-884
,共9页
图像去噪%图像修复%椒盐噪声%中值滤波%非局部变分修复
圖像去譟%圖像脩複%椒鹽譟聲%中值濾波%非跼部變分脩複
도상거조%도상수복%초염조성%중치려파%비국부변분수복
image denoising%image inpainting%salt and pepper noise%median filtering%non-local variational inpainting
分析了中值滤波及其改进型算法在处理高密度椒盐噪声时效果不理想的原因，采用变分修复方法来去除高密度椒盐噪声，基于现有的全变差修复模型提出了非局部全变差修复模型。该模型利用椒盐噪声特点（均匀分布、灰度值为0或255），将噪声点看成是图像中遗失或是破损的点，首先在图像中寻找与噪声点邻域相似的区域，将相似区域的中心像素作为噪声点新的邻域然后对其插值，把图像降噪问题转化为图像修复问题，从而达到去除高密度噪声的目的。实验结果表明：该模型对噪声密度为90％的彩色和灰度图像去噪后，其峰值信噪比为22.85和28.77，在客观评价标准方面优于中值滤波及其改进型算法。该模型能有效去除高密度下的椒盐噪声并较好地恢复图像细节，为图像去除高密度噪声提供了一种新的途径。
分析瞭中值濾波及其改進型算法在處理高密度椒鹽譟聲時效果不理想的原因，採用變分脩複方法來去除高密度椒鹽譟聲，基于現有的全變差脩複模型提齣瞭非跼部全變差脩複模型。該模型利用椒鹽譟聲特點（均勻分佈、灰度值為0或255），將譟聲點看成是圖像中遺失或是破損的點，首先在圖像中尋找與譟聲點鄰域相似的區域，將相似區域的中心像素作為譟聲點新的鄰域然後對其插值，把圖像降譟問題轉化為圖像脩複問題，從而達到去除高密度譟聲的目的。實驗結果錶明：該模型對譟聲密度為90％的綵色和灰度圖像去譟後，其峰值信譟比為22.85和28.77，在客觀評價標準方麵優于中值濾波及其改進型算法。該模型能有效去除高密度下的椒鹽譟聲併較好地恢複圖像細節，為圖像去除高密度譟聲提供瞭一種新的途徑。
분석료중치려파급기개진형산법재처리고밀도초염조성시효과불이상적원인，채용변분수복방법래거제고밀도초염조성，기우현유적전변차수복모형제출료비국부전변차수복모형。해모형이용초염조성특점（균균분포、회도치위0혹255），장조성점간성시도상중유실혹시파손적점，수선재도상중심조여조성점린역상사적구역，장상사구역적중심상소작위조성점신적린역연후대기삽치，파도상강조문제전화위도상수복문제，종이체도거제고밀도조성적목적。실험결과표명：해모형대조성밀도위90％적채색화회도도상거조후，기봉치신조비위22.85화28.77，재객관평개표준방면우우중치려파급기개진형산법。해모형능유효거제고밀도하적초염조성병교호지회복도상세절，위도상거제고밀도조성제공료일충신적도경。
The reasons of ineffectiveness of median filtering and its improved algorithm for eliminating the high-density salt-and-pepper noise are analyzed .A variational inpainting method is adopted to remove the high-density salt-and-pepper noise , and a inpainting model of Non-local Total Variation ( NL-TV) based on the existing model of Total Variation ( TV) is proposed in this article .In the NL-TV model based on the character-istics of salt-and-pepper noise ( uniform distribution and the gray value of 0 or 255 ) , we view the noise points as the lost or damaged points of an image to find the districts similar to the neighborhoods of noise points in an image, and then interpolate the noise points by taking the central pixel in a similar district as a new neighbor -hood of noise points .By this method , we transform the problem of image denoising into a problem of image restoration to remove the high-density noise .The experimental results show that the Peak Signal to Noise Rati-os(PSNRs) are 22.85 and 28.77 after removing the noise for a color and gray-scale image with 90%of noise density , which is better than the results obtained by median filter and its improved algorithm in terms of the objective evaluation criteria .Using this model , we can effectively remove the high-density salt-and-pepper noise and restore the image details better , which provides a new approach to remove the high-density noise .
