Based on the framework of regularization variation

we proposed a superresolution algorithm of SAR image targets using variable norms.Considering the sparseness of scatterings in the scene

the generalized Gaussian distribution(GGD) is used to model the amplitude of the targets data.Following Bayesian framework

we deduced the relationship between

l

p

norm regularization and the shape parameter of GGD.Under an iterative scheme

we associated the parameter

p

with the estimated results of each iteration step.The estimated parameter

p

was then used to modify the model and to construct a superresolution model with variable

l

p

norms.Our method overcomes the shortcomings of the fixed

l

p

norm regularization m

odel

which selects the parameter

p

by experience.Finally

we demonstrated the performance of our method on simulated and real SAR scene.Experiments show that the method selects parameter

p

automatically in an iterative way

and produces super-resolved SAR images.