SRGAN
Implementation of SRGAN for photo-realistic single image super-resolution using generative adversarial networks.
What is SRGAN?
SRGAN is an open-source implementation of the SRGAN (Super-Resolution Generative Adversarial Network) architecture for single image super-resolution. It upscales low-resolution images by 4x while generating photo-realistic details using adversarial training. The project solves the problem of producing blurry or artifact-prone results common in traditional super-resolution methods.
Researchers and developers working on computer vision, image processing, or generative models who need to enhance image resolution with realistic details. It's also suitable for those exploring GAN applications in super-resolution.
Developers choose SRGAN for its faithful implementation of the original paper, multi-framework support via TensorLayerX, and pre-trained models that deliver state-of-the-art visual quality. Its flexibility with datasets and backends makes it adaptable for various research and production scenarios.
Overview
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Use Cases
Best For
- Enhancing low-resolution images for photo restoration
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