MahanVeisi8/From-Chaos-to-Clarity-Denoising-Images-with-UNet-and-GANs
✨ Dive into image denoising magic! This project uses Attention U-Net and PatchGAN to tackle noise types like low Gaussian and salt-and-pepper noise. Perfect for computer vision, deep learning, and generative modeling enthusiasts. Restore clarity to noisy images with cutting-edge AI! 🚀🎨
This project helps remove various types of visual clutter, like blurriness or speckles, from grayscale facial images. It takes in a noisy facial image and outputs a clear version, making it easier to analyze emotional expressions. Anyone working with facial image datasets for emotion recognition or similar analysis, such as researchers or data scientists, would find this useful.
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Use this if you need to clean up grayscale facial images degraded by common noise types (low/high Gaussian or salt-and-pepper noise) to improve the accuracy of downstream analysis like emotion recognition.
Not ideal if your images are color, not facial, or suffer from complex degradation beyond the specified noise types.
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Jan 23, 2025
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