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Academic Journal of Computing & Information Science, 2024, 7(1); doi: 10.25236/AJCIS.2024.070108.

Medical Images Enhancement by Integrating CLAHE with Wavelet Transform and Non-Local Means Denoising


Xiwei Liu, Thao Do Chi Nguyen

Corresponding Author:
Xiwei Liu

Mohamed Bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, UAE


Enhancement of medical images is a critical aspect in the medical field, aiming to improve the visual quality and interpretability of images for both human experts and computational analysis. This paper introduces a novel method that utilizes the strengths of Contrast Limited Adaptive Histogram Equalization (CLAHE), Wavelet Transform, and Non-Local Means (NLM) Denoising. While CLAHE enhances contrast, the Wavelet Transform provides multi-level image decomposition, and Non-Local Means Denoising effectively reduces image noise. This integrated approach overcomes the limitations of using these techniques in isolation, offering a comprehensive solution for medical image enhancement. Our method demonstrated significant improvements in image clarity and detail, particularly on FracAtlas and MURA datasets, indicating its potential for enhancing diagnostic accuracy and medical image analysis.


Medical Image Enhancement, Contrast Limited Adaptive Histogram Equalization, Wavelet Transform, Non-Local Means Denoising

Cite This Paper

Xiwei Liu, Thao Do Chi Nguyen. Medical Images Enhancement by Integrating CLAHE with Wavelet Transform and Non-Local Means Denoising. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 1: 52-58. https://doi.org/10.25236/AJCIS.2024.070108.


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