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自适应双树复小波遥感图像复原
Adaptive dual-tree complex wavelet algorithm for remote sensing image restoration
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摘要: 由于遥感图像先验知识难以获取,提出了一种自适应的双树复小波迭代收缩复原算法。该算法根据模糊程度和噪声程度估计正则化参数,并利用经验公式计算收缩阈值。在实际应用中,算法能有效解决两步迭代算法使用固定参数的缺点,从而达到提高图像复原质量的目的。实验表明：相对于两步迭代算法,该算法复原图像的峰值信噪比提高0.64~12.23 dB,收敛速度提高1.4~16倍；同时,算法在提高图像复原质量、抑制噪声干扰及减少计算时间方面优势明显。Abstract: An adaptive dual-tree complex wavelet algorithm was proposed to solve the classical image restoration problem. This method is more suited to the situation that a priori information of remote-sensing image is hard to obtain.The algorithm es-timates regularization parameter from both the blurred level and the noise level,and estimates the noise using an empirical formu-la.In practical applications,the algorithm can effectively overcome the drawback of the two-step iterative shrinkage algorithm due to the use of a fixed parameter,and better imagery restoration quality could be obtained.Experimental results show that the im-age peak SNR improves 0.64-12.23 dB and the convergence speed improves 1.4-16 times.The a lgorithm has apparent advanta-ges with respect of producing better restoration results,noise disturbance suppression and the reduction of computation time.
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