CAJ | 학술논문

传统的合成孔径雷达（SAR）图像非局部均值去噪算法中，对于噪声块的相似性度量都简化为噪声点之间的相似性级联。在加性噪声模型下取得了很好的去噪效果。笔者将这种思想推广到了SAR图像的乘性噪声模型下，并且将其在最大似然权重估计的框架下对基于概率斑点（PPB）进行了改进。由于在PPB算法中参数的设置复杂，且不能自适应地获得最优效果，文中提出了基于粒子群优化的参数自适应的SAR图像去噪非局部算法。最后，在真实的SAR数据上，对该算法进行了实验测试，并与经典的 PPB算法进行了实验对比。实验验证了该算法能更好地抑制噪声并且同时保持细节信息。
전통적합성공경뢰체（SAR）도상비국부균치거조산법중，대우조성괴적상사성도량도간화위조성점지간적상사성급련。재가성조성모형하취득료흔호적거조효과。필자장저충사상추엄도료SAR도상적승성조성모형하，병차장기재최대사연권중고계적광가하대기우개솔반점（PPB）진행료개진。유우재PPB산법중삼수적설치복잡，차불능자괄응지획득최우효과，문중제출료기우입자군우화적삼수자괄응적SAR도상거조비국부산법。최후，재진실적SAR수거상，대해산법진행료실험측시，병여경전적 PPB산법진행료실험대비。실험험증료해산법능경호지억제조성병차동시보지세절신식。
In the traditional SAR image nonlocal means denoising algorithms , the patch similarity is measured by the accumulation of the pixel similarities , and a good denoising performance can be obtained for the additive noise model . This paper extends this idea to the multiplicative noise model for the SAR image , and improves the PPB ( Probabilistic Patch‐Based ) algorithm under the weighted maximum likelihood estimation framework . Since the parameters setting in the PPB algorithm is complicated and it cannot adaptively get the best performance , this paper proposes a particle swarm optimization based parameter adaptive nonlocal means algorithm for SAR image denoising . Finally , experiments compared with the canonical PPB method on the real SAR image are carried out . Experiments demonstrate that the proposed method has a good performance in speckle reduction and details preservation .