Search

In order to improve the accuracy of ballistic missile against ships, it is of great significance to study and analyze the sea targets detection of missile-borne SAR. Aiming at the problem that the detection algorithm based on multi-scale wavelet transform is difficult to accurately realize multi-target detection and recognition in the sea under coherent noise, a multi-target detection model based on improved wavelet transform is proposed. Firstly, the traditional wavelet transform based on gradient mode value is improved by using the directional adjustable wavelet function, and a multi-scale wavelet detection model with adjustable direction is constructed to roughly extract the target edge. Then, aiming at the problem that the distinction in the image is not obvious and there are a lot of sharp points, the combination processing of opening operation, Top-hat and Bot-hat filtering and small radius closed operation is used to enhance the distinction. On this basis, aiming at the porous phenomenon existing in the target, we construct a porous filling model based on erosion and complementary intersection to complete the hole filling quickly and realize multi-target edge detection. Finally, the edge detections of islands and ships are compared with three common algorithms to verify the superiority of this model.

We propose an adaptive total generalized variation (TGV) based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising. The variable splitting (VS) and the classical augmented Lagrangian method (ALM) are used to solve the proposed model. With the proposed adaptive model and ALM, the regularization parameter, which balances the data fidelity and the regularizer, is refreshed with a closed form in each iterate, and the image denoising can be accomplished without manual interference. Numerical results indicate that our method is effective in staircasing effect suppression and holds superiority over some other state-of-the-art methods both in quantitative and in qualitative assessment.