Image Reconstruction in Compressed Remote Sensing with Low-rank and L1-norm Regularization
نویسندگان
چکیده
In this paper, we proposed a new model with nuclear-norm and L1-norm regularization for image reconstruction in aerospace remote sensing. The curvelet based L1-norm regularization promotes sparse reconstruction, while the low-rank based nuclear-norm regularization leads to a principle component solution. Split Bregman method is used to solve this problem. Numerical experiments show the proposed model achieves better reconstruction results compared with the previous model with L1-norm regularization.
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