红外与激光工程
紅外與激光工程
홍외여격광공정
INFRARED AND LASER ENGINEERING
2013年
11期
3150-3155
,共6页
齐蕾%张闻文%陈钱%顾国华
齊蕾%張聞文%陳錢%顧國華
제뢰%장문문%진전%고국화
中值滤波%模糊推理%隶属函数%自适应%EMCCD图像处理
中值濾波%模糊推理%隸屬函數%自適應%EMCCD圖像處理
중치려파%모호추리%대속함수%자괄응%EMCCD도상처리
median filtering%fuzzy reasoning%membership function%adaptive%image processing of EMCCD
针对电子倍增CCD(EMCCD)图像噪声密度随着增益的变化而变化,提出了一种基于噪声点检测的自适应模糊中值滤波算法。该算法由模糊滤波模块和自适应模块两部分组成。首先,该算法对滤波窗口内的中心点进行噪声检测;然后对检测为噪声的像素点引入双阈值,并根据引入的阈值和滤波窗口内的中值建立噪声点的模糊隶属函数,根据模糊隶属函数对噪声点进行滤波处理后输出;最后采用自适应模块调整待处理图像的像素。仿真及实验结果表明,新算法不仅能够有效地将图像中的噪声去除,而且很好地保护了图像中的细节和边缘,PSNR比传统的自适应中值滤波算法平均提高了15 dB以上;该算法在低噪声密度情况下性能明显好于其他中值滤波器,在高噪声密度情况下性能也比较稳定。
針對電子倍增CCD(EMCCD)圖像譟聲密度隨著增益的變化而變化,提齣瞭一種基于譟聲點檢測的自適應模糊中值濾波算法。該算法由模糊濾波模塊和自適應模塊兩部分組成。首先,該算法對濾波窗口內的中心點進行譟聲檢測;然後對檢測為譟聲的像素點引入雙閾值,併根據引入的閾值和濾波窗口內的中值建立譟聲點的模糊隸屬函數,根據模糊隸屬函數對譟聲點進行濾波處理後輸齣;最後採用自適應模塊調整待處理圖像的像素。倣真及實驗結果錶明,新算法不僅能夠有效地將圖像中的譟聲去除,而且很好地保護瞭圖像中的細節和邊緣,PSNR比傳統的自適應中值濾波算法平均提高瞭15 dB以上;該算法在低譟聲密度情況下性能明顯好于其他中值濾波器,在高譟聲密度情況下性能也比較穩定。
침대전자배증CCD(EMCCD)도상조성밀도수착증익적변화이변화,제출료일충기우조성점검측적자괄응모호중치려파산법。해산법유모호려파모괴화자괄응모괴량부분조성。수선,해산법대려파창구내적중심점진행조성검측;연후대검측위조성적상소점인입쌍역치,병근거인입적역치화려파창구내적중치건립조성점적모호대속함수,근거모호대속함수대조성점진행려파처리후수출;최후채용자괄응모괴조정대처리도상적상소。방진급실험결과표명,신산법불부능구유효지장도상중적조성거제,이차흔호지보호료도상중적세절화변연,PSNR비전통적자괄응중치려파산법평균제고료15 dB이상;해산법재저조성밀도정황하성능명현호우기타중치려파기,재고조성밀도정황하성능야비교은정。
The noise density of Electron Multiplying CCD (EMCCD) image varies with the gain, a noise detection based on adaptive fuzzy median filter (AFMF) algorithm was proposed. The algorithm consisted of fuzzy filtering module and adaptive module. First, the noise pixels in the center of the filter window was identified. Second, the double thresholds were introduced for these detected "noise points", basing on the thresholds and median of the filtering window, the fuzzy membership function of noise points was put forward, and the fuzzy membership function was utilized to filter the noise points. Finally, the adaptive module was used to adjust the pixel in the image. Simulation and experimental results indicate that the new algorithm is able to remove noise pixels effectively and protect the details well in the image. Compared with the adaptive median filtering, the average PSNR improves at least 15 dB. The performance is better than the other median filters under the condition of low noise density and relatively stable under the condition of high noise density.