This method introduces a series of neighborhood models which reflect local edge property to describe image details 該方法利用一系列反映局部邊界特征的鄰域模型來描還圖像的細(xì)節(jié)。
Second , a new wavelet - based denoising method without free parameters is proposed which is based on the sparseness and decorrelation properties of the discrete wavelet transform 首先提出了一種基于相關(guān)鄰域模型的sar圖像rcs重構(gòu)方法。其次,利用離散小波變換的稀疏性和減相關(guān)性進(jìn)行sar圖像濾波。
3 , on the base of the traditional spatial filtering , the author present , a new despeckle algorithm , that make use of iterated processing and correlated neighbourhood model , iterated filtering method of the sar image combining the correlated neighbourhood model with maximum a posteriori filter . first , a series of templates refecting direction information are established and every template is present for a kind of neighbour structure . then on the basis of sar images statistical property , the maximum a posteriori estimate of the real intensity under observation image values is got by bayes formulatio - n 3 、針對傳統(tǒng)空間濾波器的不足,引入迭代處理和相關(guān)鄰域模型的概念,提出了基于相關(guān)鄰域模型的最大后驗(yàn)迭代濾波。該算法引用一系列反映局部邊界特征的鄰域模型,以描述圖像的細(xì)節(jié)。引入強(qiáng)度的先驗(yàn)概率分布模型,利用bayes方法,對各個(gè)結(jié)構(gòu)進(jìn)行實(shí)際強(qiáng)度的最大后驗(yàn)估計(jì)。