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Abstract
This article analyzes methods for detecting and effectively removing noise in medical images to restore high-quality diagnostic images. Common noise types such as Gaussian, salt and pepper, and speckle noise are examined in detail due to their frequent occurrence in MRI, CT, and ultrasound modalities. Both classical filtering methods (such as median and Gaussian filters) and advanced techniques (including Non-Local Means, Wavelet Denoising, and Deep Learning-based approaches) are discussed for their effectiveness in enhancing image clarity. Practical implementations using the Python programming language are provided to demonstrate the application of these techniques in real-world scenarios. The study also includes comparative visual results before and after denoising to assess improvements in image quality. This work serves as a practical guide for researchers and developers working on medical image processing and reconstruction.
References
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