Higher-order MRFs based image super resolution: why not MAP?

A trainable filter-based higher-order Markov random fields model - the so called fields of experts (FoE), has proved a highly effective image prior model for many classic image restoration problems. Generally, two options are available to incorporate the learned FoE prior in the inference procedure: (i) sampling-based minimum mean square error (MMSE) estimate, and (ii) energy minimisation-based maximum a posteriori (MAP) estimate. This study is devoted to the FoE prior based single image super resolution (SR) problem, and the author suggest to make use of the MAP estimate for inference based on two facts: (i) It is well-known that the MAP inference has a remarkable advantage of high computational efficiency, while the sampling-based MMSE estimate is very time consuming. (ii) Practical SR experiment results demonstrate that the MAP estimate works equally well compared with the MMSE estimate with exactly the same FoE prior model. Moreover, it can lead to even further improvements by incorporating the discriminatively trained FoE prior model. In summary, the author hold that for higher-order natural image prior based SR problem, it is better to employ the MAP estimate for inference.