Bayesian image interpolation using Markov random fields driven by visually relevant image features

In this paper we present a Markov Random Field (MRF) based image interpolation procedure suited to both noise-free and noisy measurements. Specifically, after introducing a MRF characterized by means of a novel complex line process representing the visually relevant image features, we derive the global Maximum A Posteriori (MAP) interpolator under the hypothesis of spatially variant additive Gaussian noise. Besides, we derive a closed form local Bayesian MAP interpolator, on the base of which we develop a suboptimal, computationally efficient, single pass interpolation procedure. Numerical simulations demonstrate that the interpolation procedure outperforms state-of-the-art techniques, from both a subjective and objective point of view, in the case of noise-free and noisy measurements. ► We present a Markov random field based image interpolation procedure. ► Both a global and a local formulation of a MAP interpolation are derived. ► We model the visually relevant image features by a novel complex line process. ► The interpolator deals also with measurements affected by spatially variant noise.