Noise removal and intensity inhomogeneity correction in brain MRI: A comprehensive review from classical filters to...

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 architecture, is the limiting factor in that literature. On the BrainWeb T1 benchmark at Rician noise σ = 9%, 3D-Parallel-RicianNet reaches a published peak signal-to-noise ratio (PSNR) of 37.11 dB and structural similarity index (SSIM) of 0.9859, compared with 28.91 dB and 0.9584 for BM3D. The 2023-2026 generation of models-Swin-UNet Transformers, the Imaging Transformer for SNR ≪ 1, hybrid HTC-Net, the lightweight CHARMS CNN-Transformer, denoising diffusion probabilistic models and self-supervised frameworks such as Coil2Coil, SURE-Net, Rep2Rep and the Efficient Collaborative Diffusion Model-report further methodological gains and remove the requirement for paired noise-free training data, which had been the principal obstacle to clinical deployment. Open challenges remain in cross-scanner generalisation, in clinically meaningful evaluation beyond PSNR and SSIM, and in the construction of a publicly available multi-vendor benchmark for brain-MRI denoising.