💡[Feature]: Image Denoising Using Autoencoders with Keras

[deepanshubaghel]

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Feature Description

Image Denoising with Autoencoders

1. Noise Reduction: The autoencoder will take noisy images as input and learn to reconstruct clean images by filtering out unwanted noise, improving overall image clarity.

2. Model Architecture: The feature will employ a convolutional autoencoder model that includes an encoder for compressing the image and a decoder for reconstructing it, ensuring high-quality outputs.

3. User Experience: Users can upload noisy images, and the model will automatically process them, providing a clear, denoised version in a matter of seconds.

4. Applications: This feature can be particularly beneficial for fields like photography, medical imaging, and any scenario where image clarity is crucial.

Use Case

The image denoising feature using autoencoders would boost project quality by delivering clearer images, improving model accuracy, and saving users time with an automated solution. This flexibility makes it valuable across different fields, enhancing user experience and learning opportunities.

Benefits

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Priority

High

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• I have read the Contributing Guidelines
• I'm a GSSOC'24 contributor
• I want to work on this issue

[github-actions]

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊

Note: I Maintain the repo issue twice a day, or ideally 1 day, If your issue goes stale for more than one day you can tag and comment on this same issue.

You can also check our CONTRIBUTING.md for guidelines on contributing to this project.
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[sanjay-kv]

#1367

[github-actions]

Hello @deepanshubaghel! Your issue #1399 has been closed. Thank you for your contribution!