Selection of regularization parameter based on generalized cross-validation in total variation remote sensing image restoration
Peng Liu, Dingsheng Liu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
In this article, we apply the total variation method to remote sensing image restoration. A new method to calculate the regularization parameter by using an improved Generalized Cross-Validation (GCV) method is proposed. Classical GCV can not be directly used in total variation regularization due to the nonlinearity of the total variation. In our method, the GCV method and the fixed point iterative method are combined. In order to use the GCV method, we separate a new linear regularization operator from the definition of the fixed point iterative method. Based on the linear regularization operator, we change the form of the classical GCV function and make it suitable for total variation regularization. A new GCV function suitable for total variation regularization is constructed. By using the new GCV method, the regularization parameter is automatically changing in total variation remote sensing image restoration and a higher signal-noise-ratio is acquired. Experiments confirm that the adaptability and the stability of the total variation remote sensing image restoration are improved.