A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model

A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model

Blog Article

Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar.However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise.Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy.In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model.

Compared with the traditional super-resolution method which is focused on one-dimensional Shower Shelf (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene.By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system.Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework.Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function.

Validation experiments using both Body Spray synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.