An approach for quality assessment of the filtered images was proposed . after qualitative and quantitative assessment , some conclusions have been drawn : ( 1 ) spatial filters are affected greatly by filtering window size , the size of 5 5 presents best result ; ( 2 ) enhanced frost filter , enhanced lee filter and gamma - map filter smooth the speckle noise well with a small sacrifice of losing texture details and narrow edges , which are better than other popular spatial filters ; ( 3 ) the results show that the proposed filter retains texture and edges well , so more researches are expected 通过对jers - 1sar图像的各种滤波图像进行了目视评价和指标比较，得出如下结论: ( 1 )各种空间滤波器受滤波窗口的影响很大， 5 5大小的滤波窗口滤波效果较好； ( 2 )对比各种常用的空间滤波器，增强的frost滤波器、增强的lee滤波器和gamma - map滤波器在去噪和纹理、结构特征保持方面效果最好； ( 3 )小波滤波方法在保持sar图像纹理细节方面表现出了很好的优势，还有待进一步研究。
Based on the multiplicative speckle model , a new filtering algorithm based on wavelet transform was developed . first , the multiplicative speckle noise was changed by logarithmic transform into additive noise , which simplified the subsequent speckle suppression . the speckle suppression was carried out in the process of wavelet transform of the logarithmic transformed image 针对斑点噪声的乘性模型特点，构造了一种基于小波分析的滤波器：对sar图像进行对数变换，利用小波变换对对数图像进行滤波处理，再通过指数变换得到去噪图像。
It can provide higher speed and alleviate the spectral degradation which usually goes with the existing algorithms , as well as constrain speckle noise of radar images . in respect of image fusion algorithm , the fusion rules and operators are essential . a new set of fusion rules and corresponding operators are given in this thesis 实验证明，在融合源图象特性差异较大的情况下，该算法与已有的基于小波分析的遥感图象融合算法相比较性能更优，具有运算速度快、减少光谱退化的特点，并能在一定程度上抑制相干斑噪声。
In the pretreatment of these images , the speckle noise model has been improved in this dissertation . on the basis of traditional methods of neighborhood averaging and low - pass filtering , the methods of adaptive histogram enhancement and the improved adaptive weighted median filter have been presented , and the noise has been reduced greatly 在图像的预处理的过程中，论文首先改进了超声图像的speckle噪声模型，其次在借鉴传统的方法的基础上，提出并实现了自适应图像增强和自适应滤波处理方法，较好地抑制了噪声。
