super-image and image-super-resolution
These are **competitors**: both provide PyTorch-based image super-resolution model implementations with similar functionality (Residual Dense Networks, adversarial training), so users would typically choose one based on documentation quality, model variety, and active maintenance rather than using both together.
About super-image
eugenesiow/super-image
Image super resolution models for PyTorch.
This project helps graphic designers, photographers, or anyone working with visuals to enhance the quality of low-resolution images. It takes a small, pixelated image and outputs a larger, sharper version, improving details and clarity without distortion. The end user is anyone who needs to upscale images for print, web, or digital display, such as content creators, marketing professionals, or scientists working with visual data.
About image-super-resolution
idealo/image-super-resolution
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
Keras-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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