  Recent progress in compressive sensing states the importance of exploiting
intrinsic structures in sparse signal reconstruction. In this letter, we
propose a Markov random field (MRF) prior in conjunction with fast iterative
shrinkagethresholding algorithm (FISTA) for image reconstruction. The MRF prior
is used to represent the support of sparse signals with clustered nonzero
coefficients. The proposed approach is applied to the inverse synthetic
aperture radar (ISAR) imaging problem. Simulations and experimental results are
provided to demonstrate the performance advantages of this approach in
comparison with the standard FISTA and existing MRF-based methods.
