SINGLE-FRAME SIGNAL RECOVERY USING A SIMILARITY-PRIOR BASED ON PEARSON TYPE VII MRF

摘要

We consider the problem of signal reconstruction from noisy observations in a highly under-determined problem setting. Most of previous work does not consider any specific extra information to recover the signal. Here we address this problem by exploiting the similarity between the signal of interest and a consecutive motionless frame. We incorporate this additional information of similarity that is available into a probabilistic image prior based on the Pearson type VII Markov Random Field model. Results on both synthetic and real data of MRI images demonstrate the effectiveness of our method in both compressed setting and classical super-resolution experiments.