Nowadays, medical image fusion serves as a significant aid for the precise diagnosis or surgical navigation. In this paper, we propose a novel tensor factorization based fusion strategy which well combines the multimodal, multiscale nature of medical images and multiway structure of tensors. Since our model adopts the sparse representation (SR) prior, we suffer from the systematic underestimation of the true solution because of the L1-norm regularization term. To address this problem, we introduce the generalized minimax-concave (GMC) penalty into our framework, which is a non-convex regularization term itself. It is beneficial for the whole cost function to maintain convexity. Furthermore, we combine the alternating direction method of multipliers (ADMM) algorithm and forward-backward (FB) method to achieve the optimization process. We conduct extensive experiments on five kinds of practical medical image fusion problems with 96 pairs of images in total. The results confirm that our model has great improvements in visual performance and objective metrics against the existing methods.
Spacecraft cluster flight, which is a novel multi-spacecraft flight mode, has become an important research direction of distributed space system for the future, especially has unique advantage in continuous detection on area. Aiming at the satellite group formed by several satellite clusters, an orbit design method satisfying the long-term continuous and stable area-coverage is proposed. The basic dynamic model is built under the influence of J2 perturbation, the calculation method of "node period" and "node day" is put forward and then the long-term continuous stable group orbit initialization design conditions are built. Validation of orbit design is made under the typical scenario and through the STK simulation, the simulation results show that this design method can realize the fixed time revisiting and long-term stability of coverage of the target area.
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