CAJ | 학술논문

近年来多传感器数据融合技术在图象处理领域得到广泛的重视和应用.鉴于来自同一景物的多幅变形图象，其来源不同，每幅图象都带有不同的噪声，针对这种图象的恢复提出了一种基于自组织特征映射神经网络的图象融合算法.该算法可分为3步，第1步是图象的预处理阶段，即对图象进行加权中值滤波,去除部分噪声；第2步利用自组织神经网络对每幅图象的象素进行聚类分析；第3步，对第2步得到的结果按照一定规则进行融合.仿真结果表明，该算法能明显提高图象质量.
근년래다전감기수거융합기술재도상처리영역득도엄범적중시화응용.감우래자동일경물적다폭변형도상，기래원불동，매폭도상도대유불동적조성，침대저충도상적회복제출료일충기우자조직특정영사신경망락적도상융합산법.해산법가분위3보，제1보시도상적예처리계단，즉대도상진행가권중치려파,거제부분조성；제2보이용자조직신경망락대매폭도상적상소진행취류분석；제3보，대제2보득도적결과안조일정규칙진행융합.방진결과표명，해산법능명현제고도상질량.
Multisensor data fusion has played an important role in image processing recently. For some images from the same scene, each of them has different noise because of their different sources. This paper presents a new kind of image data fusion algorithm based on the self-organizing feature map neural network. This algorithm can be performed with three steps. In the first step，the pretreatment of the images is performed by the weighted-median filter in order to remove some noise. In the second stage we use self-organizing feature map neural network to cluster the pixels of each image and then extend hard partition into fuzzy partition. In the third stage, we fuse the data from the last step in conformity to a certain rule. The simulation results illustrate that this new algorithm can improve the quality of the image distinctly and the pretreatment of the images can improve the fusion result efficiently.