Adaptive dual-tree complex wavelet algorithm for remote sensing image restoration

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 estimates regularization parameter from both the blurred level and the noise level, and estimates the noise using an empirical formula. 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 image peak SNR improves 0.64-12.23 dB and the convergence speed improves 1.4-16 times. The algorithm has apparent advantages with respect of producing better restoration results, noise disturbance suppression and the reduction of computation time.