کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
536970 | 870651 | 2013 | 17 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Bayesian image interpolation using Markov random fields driven by visually relevant image features Bayesian image interpolation using Markov random fields driven by visually relevant image features](/preview/png/536970.png)
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.
Journal: Signal Processing: Image Communication - Volume 28, Issue 8, September 2013, Pages 967–983