KupynOrest/DeblurGAN

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2638 stars · 531 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of the DeblurGAN paper for blind motion deblurring using conditional adversarial networks. It uses a Conditional Wasserstein GAN with gradient penalty and VGG-19 perceptual loss to convert blurry images into sharp estimates.

Use cases

• deblur blurry photos with a GAN
• remove motion blur from images
• reproduce the DeblurGAN paper results
• train an image-to-image translation model in PyTorch
• adapt a conditional WGAN for super resolution or dehazing

When to choose

• you need research-grade motion deblurring with pretrained weights
• you want a PyTorch GAN codebase to extend for image-to-image translation tasks
• you are reproducing or citing the DeblurGAN paper

When to avoid

• you need CPU-only inference (untested)
• you want a production-ready, actively maintained deblurring product
• you need a permissively licensed library (license is non-standard)

Facets

library · maturity maintenance

image-processing computer-vision deep-learning computer-vision image-processing deep-learning python gan deblurring pytorch image-to-image-translation pix2pix research-code gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="KupynOrest/DeblurGAN")