idealo/image-super-resolution
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
ArchivedKeras-based implementation of Residual Dense Networks (RDN/RRDN) for single-image super-resolution, supporting both PSNR-driven and adversarial training with perceptual loss via VGG19 feature extraction. Includes pre-trained models for different use cases (standard upscaling, artifact cancellation, photo-realistic GAN output) and handles large images through patch-based inference to avoid memory constraints. Provides Docker and AWS cloud training pipelines alongside Jupyter notebooks for rapid experimentation.
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Language
Python
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Apache-2.0
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Last pushed
Dec 18, 2024
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