This paper presents a novel framework for denoising magnetic resonance images. The framework relies on adaptive Markov-random-field (MRF) image models that we infer nonparametrically from the corrupted input data itself. The proposed denoising method produces an optimal reconstruction based on principles in empirical-Bayesian estimation and information theory. Given the corrupted input data and...

Traditional denoising schemes require prior knowledge or statistics of the noise corrupting the signal, or estimate the noise from noise-only portions of the signal, which requires knowledge of speech boundaries. Extending denoising methods to perform well in unknown noise conditions can facilitate processing of data captured in different real life environments, and relax rigid data acquisition...

In this study, an improved method based on evolutionary algorithms for denoising of satellite images is proposed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function...