Noise removal and intensity inhomogeneity correction in brain MRI: A comprehensive review from classical filters to transformer and diffusion models
Abstract
Magnetic resonance imaging (MRI) is the preferred modality for non-invasive examination of the human brain, but the acquired images are inevitably corrupted by Rician noise and by intensity non-uniformity (INU), also called the bias field, which arises from imperfect radio-frequency coils and from subject-induced field perturbations, and these artefacts degrade subsequent registration, segmentation and tumour-detection pipelines. This article reviews 151 publications spanning four decades (1985 to early 2026) on the removal of noise and intensity inhomogeneity from brain MRI, located through a structured search of IEEE Xplore, PubMed, Scopus, Web of Science and Google Scholar and complemented by manual snowballing. The methods surveyed are organised into prospective approaches (phantom-based, multi-coil and special-sequence techniques), retrospective approaches (filtering, surface fitting, segmentation-based and histogram-based correction), classical machine learning, and deep learning (convolutional networks, generative adversarial networks, denoising autoencoders, transformer-based architectures, denoising diffusion probabilistic models and self-supervised frameworks). Deep-learning approaches to bias-field correction are reviewed separately and classified into direct field regression, adversarial translation, joint estimation of the field and the uniform image, and frequency-domain probabilistic formulations, a distinction that clarifies why supervision, rather than…
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