Zooming Into Clarity: Image Denoising Through Innovative Autoencoder Architectures

Zooming Into Clarity: Image Denoising Through Innovative Autoencoder Architectures

<p dir="ltr">In today’s era of increasing data complexity and pervasive noise, robust techniques for data processing, reconstruction, and denoising are crucial. Autoencoders, known for their adaptability in unsupervised learning, offer a strategic solution to these challenges. This r...

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Bibliographic Details
Main Author: Khatereh Mohammadi (17309662) (author)
Other Authors: Ashhadul Islam (16869981) (author), Samir Brahim Belhaouari (9427347) (author)
Published: 2024
Subjects:
Information and computing sciences
Artificial intelligence
Computer vision and multimedia computation
Machine learning
Autoencoders
data reconstruction
noise reduction
loss function
architecture
high-resolution imaging enhancement
Noise reduction
Image reconstruction
Convolutional codes
Feature extraction
Task analysis
Image enhancement
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