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 ABSTRACT

This project is aimed at overcoming the problem of image denoising when it comes to medical images such as X-rays, MRI, CT scans and Ultrasounds, in which noise affects diagnosing decisions. Most of traditional denoising methods, such as Median filter, Gaussian filter, Average filter, and Bilateral Filter, could hardly remove noise while preserving image details. For this reason, the project looks at using autoencoders that are mainly unsupervised neural networks, specifically convolutional autoencoders. These systems perform excellently in image filtering and generation, delivering sharp images to the user from deformed or scarce inputs while maintaining space fidelity. On efficient optimization in complex scenarios, we use the Adam Optimizer. The development of technology opens new opportunities for improving the use of data, including the medical imaging and computer vision fields among others.

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