(untitled) (Journal of the College of Basic Education, 2025)
Denoising medical images is a key step in preprocessing for accurate interpretation. Previously, several solutions with varying degrees of noise reduction efficacy were offered. Deep learning algorithms have demonstrated significant promise in recent years, outperforming traditional convolutional approaches on many instances. However, these complex algorithms usually require large training datasets and incur significant processing costs. In this study, we look at the usage of convolutional layers in medical image denoising, even with tiny sample sizes. Combining several photos allowed us to easily expand the amount of the dataset while also improving denoising speed. Our findings indicate that even tiny networks may successfully repair severely damaged images, achieving a degree of clarity where noise is scarcely detectable.