asp
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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