Open Access

REVIEW

From Spatial Domain to Patch-Based Models: A Comprehensive Review and Comparison of Multimodal Medical Image Denoising Algorithms

Apoorav Sharma¹, Ayush Dogra^2,*, Bhawna Goyal³, Archana Saini², Vinay Kukreja²

1 University School of Computing, Sunstone, Rayat Bahra University, Mohali, 140104, Punjab, India
2 Chitkara University Institute of Engineering & Technology, Chitkara University, Rajpura, 140401, Punjab, India
3 Marwadi University Research Centre, Department of Engineering, Marwadi University, Rajkot, 360003, Gujarat, India

* Corresponding Author: Ayush Dogra. Email:

Computers, Materials & Continua 2025, 85(1), 367-481. https://doi.org/10.32604/cmc.2025.066481

Received 09 April 2025; Accepted 26 July 2025; Issue published 29 August 2025

Abstract

To enable proper diagnosis of a patient, medical images must demonstrate no presence of noise and artifacts. The major hurdle lies in acquiring these images in such a manner that extraneous variables, causing distortions in the form of noise and artifacts, are kept to a bare minimum. The unexpected change realized during the acquisition process specifically attacks the integrity of the image’s quality, while indirectly attacking the effectiveness of the diagnostic process. It is thus crucial that this is attended to with maximum efficiency at the level of pertinent expertise. The solution to these challenges presents a complex dilemma at the acquisition stage, where image processing techniques must be adopted. The necessity of this mandatory image pre-processing step underpins the implementation of traditional state-of-the-art methods to create functional and robust denoising or recovery devices. This article hereby provides an extensive systematic review of the above techniques, with the purpose of presenting a systematic evaluation of their effect on medical images under three different distributions of noise, i.e., Gaussian, Poisson, and Rician. A thorough analysis of these methods is conducted using eight evaluation parameters to highlight the unique features of each method. The covered denoising methods are essential in actual clinical scenarios where the preservation of anatomical details is crucial for accurate and safe diagnosis, such as tumor detection in MRI and vascular imaging in CT.

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Keywords

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